{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-20T16:26:09.930045Z",
     "start_time": "2023-08-20T16:26:09.927043Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using `tqdm.autonotebook.tqdm` in notebook mode. Use `tqdm.tqdm` instead to force console mode (e.g. in jupyter console)\n"
     ]
    },
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'more_itertools'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[1], line 5\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpyplot\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mplt\u001b[39;00m\n\u001b[0;32m      4\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mitertools\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m product\n\u001b[1;32m----> 5\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmore_itertools\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m gray_product\n\u001b[0;32m      6\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[0;32m      7\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mpd\u001b[39;00m\n",
      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'more_itertools'"
     ]
    }
   ],
   "source": [
    "import shap\n",
    "import pickle\n",
    "import matplotlib.pyplot as plt\n",
    "from itertools import product\n",
    "from more_itertools import gray_product\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-20T16:09:13.756370Z",
     "start_time": "2023-08-20T16:09:13.713447Z"
    },
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "with open(\"./data/obj_v2\", \"rb\") as obj_file:\n",
    "    obj = pickle.load(obj_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_xgb = obj[0]\n",
    "data = obj[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>707</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>29.1250</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>708</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>709</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>710</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>711</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>712 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Pclass  Sex   Age  SibSp  Parch     Fare  Cabin  Embarked  Survived\n",
       "0         3    1  22.0      1      0   7.2500      7         2         0\n",
       "1         1    0  38.0      1      0  71.2833      2         0         1\n",
       "2         3    0  26.0      0      0   7.9250      7         2         1\n",
       "3         1    0  35.0      1      0  53.1000      2         2         1\n",
       "4         3    1  35.0      0      0   8.0500      7         2         0\n",
       "..      ...  ...   ...    ...    ...      ...    ...       ...       ...\n",
       "707       3    0  39.0      0      5  29.1250      7         1         0\n",
       "708       2    1  27.0      0      0  13.0000      7         2         0\n",
       "709       1    0  19.0      0      0  30.0000      1         2         1\n",
       "710       1    1  26.0      0      0  30.0000      2         0         1\n",
       "711       3    1  32.0      0      0   7.7500      7         1         0\n",
       "\n",
       "[712 rows x 9 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-20T16:25:19.972426Z",
     "start_time": "2023-08-20T16:25:19.965423Z"
    },
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "colors = [\"brown\", \"teal\", \"blue\", \"coral\", \"limegreen\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-20T16:25:00.634334Z",
     "start_time": "2023-08-20T16:25:00.627331Z"
    },
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "def create_variable_list(col):\n",
    "    min_val = col.min()\n",
    "    max_val = col.max()\n",
    "\n",
    "    unique = col.unique()\n",
    "\n",
    "    if len(unique) < 50:\n",
    "        col_vals = sorted(list(unique))\n",
    "    else:\n",
    "        delta = (max_val - min_val) / 100\n",
    "        col_vals = []\n",
    "        while min_val <= max_val:\n",
    "            col_vals.append(min_val)\n",
    "            min_val += delta\n",
    "    return col_vals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-20T16:25:02.759164Z",
     "start_time": "2023-08-20T16:25:02.755660Z"
    },
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "def top5_find_by_importance(explainer, data):\n",
    "    shap_values = explainer(data).values\n",
    "\n",
    "    col_names = []\n",
    "    indexes = []\n",
    "    mean_importance = list(np.mean(np.absolute(shap_values), axis=0))\n",
    "\n",
    "    max_list = sorted(mean_importance)[-5:]\n",
    "    for max_val in max_list:\n",
    "        indexes.append(mean_importance.index(max_val))\n",
    "        col_names.append(data.columns[mean_importance.index(max_val)])\n",
    "\n",
    "    return col_names, indexes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-20T16:25:05.209299Z",
     "start_time": "2023-08-20T16:25:05.204113Z"
    },
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "def top5_centered_importance(explainer, data, col_name):\n",
    "    data_copy = data.copy()\n",
    "\n",
    "    col_vals = create_variable_list(data[col_name])\n",
    "    res_vals = []\n",
    "\n",
    "    for val in col_vals:\n",
    "        new_col = len(data_copy[col_name]) * [val]\n",
    "        data_copy[col_name] = new_col\n",
    "        shap_values = explainer(data_copy).values\n",
    "        res_vals.append(shap_values)\n",
    "\n",
    "    return res_vals, col_vals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-20T16:25:22.601713Z",
     "start_time": "2023-08-20T16:25:22.600209Z"
    },
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "def plot_top5_centered_importance(model, data, col_name, absolute=False):\n",
    "    plot = plt.axes()\n",
    "    plot.figure.set_size_inches(16, 8)\n",
    "\n",
    "    if absolute:\n",
    "        title = \"Центрированный график изменения абсолютной важности переменных\"\n",
    "    else:\n",
    "        title = \"Центрированный график изменения важности переменных\"\n",
    "    plot.set_title(title, fontsize=18)\n",
    "\n",
    "    explainer = shap.TreeExplainer(model)\n",
    "\n",
    "    data_copy = data.copy()\n",
    "    if \"Survived\" in data.columns:\n",
    "        data_copy = data_copy.drop(\"Survived\", axis=1)\n",
    "\n",
    "    cols, indexes = top5_find_by_importance(explainer, data_copy)\n",
    "\n",
    "    res_vals, col_vals = top5_centered_importance(explainer, data_copy, col_name)\n",
    "\n",
    "    res_vals = np.array(res_vals)\n",
    "\n",
    "    for i in range(0, len(indexes)):\n",
    "        res = []\n",
    "\n",
    "        for j in range(0, len(res_vals)):\n",
    "            val = res_vals[j, :, indexes[i]]\n",
    "            if absolute:\n",
    "                val = np.absolute(val)\n",
    "            res.append(val.mean())\n",
    "\n",
    "        plot.plot(col_vals, res, color=colors[i], linewidth=4, label=cols[i])\n",
    "\n",
    "    plot.grid()\n",
    "    plot.set_xlabel(col_name, fontsize=16)\n",
    "    plot.set_ylabel(\"Важность переменных\", fontsize=16)\n",
    "    plot.legend()\n",
    "\n",
    "    return plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-20T16:26:15.358166Z",
     "start_time": "2023-08-20T16:26:13.995434Z"
    },
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': 'Центрированный график изменения важности переменных'}, xlabel='Sex', ylabel='Важность переменных'>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_top5_centered_importance(model_xgb, data, \"Sex\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = data.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pclass  Sex   Age  SibSp  Parch     Fare  Cabin  Embarked  Survived\n",
       "0       3    1  22.0      1      0   7.2500      7         2         0\n",
       "1       1    0  38.0      1      0  71.2833      2         0         1\n",
       "2       3    0  26.0      0      0   7.9250      7         2         1\n",
       "3       1    0  35.0      1      0  53.1000      2         2         1\n",
       "4       3    1  35.0      0      0   8.0500      7         2         0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>712.000000</td>\n",
       "      <td>712.000000</td>\n",
       "      <td>712.000000</td>\n",
       "      <td>712.000000</td>\n",
       "      <td>712.000000</td>\n",
       "      <td>712.000000</td>\n",
       "      <td>712.000000</td>\n",
       "      <td>712.000000</td>\n",
       "      <td>712.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.240169</td>\n",
       "      <td>0.636236</td>\n",
       "      <td>29.642093</td>\n",
       "      <td>0.514045</td>\n",
       "      <td>0.432584</td>\n",
       "      <td>34.567251</td>\n",
       "      <td>5.825843</td>\n",
       "      <td>1.595506</td>\n",
       "      <td>0.404494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.836854</td>\n",
       "      <td>0.481420</td>\n",
       "      <td>14.492933</td>\n",
       "      <td>0.930692</td>\n",
       "      <td>0.854181</td>\n",
       "      <td>52.938648</td>\n",
       "      <td>2.136155</td>\n",
       "      <td>0.779038</td>\n",
       "      <td>0.491139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.050000</td>\n",
       "      <td>5.750000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>15.645850</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>512.329200</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Pclass         Sex         Age       SibSp       Parch        Fare  \\\n",
       "count  712.000000  712.000000  712.000000  712.000000  712.000000  712.000000   \n",
       "mean     2.240169    0.636236   29.642093    0.514045    0.432584   34.567251   \n",
       "std      0.836854    0.481420   14.492933    0.930692    0.854181   52.938648   \n",
       "min      1.000000    0.000000    0.420000    0.000000    0.000000    0.000000   \n",
       "25%      1.000000    0.000000   20.000000    0.000000    0.000000    8.050000   \n",
       "50%      2.000000    1.000000   28.000000    0.000000    0.000000   15.645850   \n",
       "75%      3.000000    1.000000   38.000000    1.000000    1.000000   33.000000   \n",
       "max      3.000000    1.000000   80.000000    5.000000    6.000000  512.329200   \n",
       "\n",
       "            Cabin    Embarked    Survived  \n",
       "count  712.000000  712.000000  712.000000  \n",
       "mean     5.825843    1.595506    0.404494  \n",
       "std      2.136155    0.779038    0.491139  \n",
       "min      0.000000    0.000000    0.000000  \n",
       "25%      5.750000    2.000000    0.000000  \n",
       "50%      7.000000    2.000000    0.000000  \n",
       "75%      7.000000    2.000000    1.000000  \n",
       "max      8.000000    2.000000    1.000000  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop(\"Survived\", axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df = df.drop(\"Fare\", axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_dataset_with_full_coverage(df):\n",
    "    creation_data = get_df_values_for_creation(df.to_dict(), 1)\n",
    "    test(creation_data)\n",
    "    product_result = product(*list(creation_data.values()))\n",
    "    res_df = pd.DataFrame(product_result, columns=df.columns)\n",
    "    return res_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n",
      "6\n",
      "480\n",
      "2880\n",
      "20160\n",
      "60480\n",
      "544320\n",
      "1632960\n"
     ]
    }
   ],
   "source": [
    "res_df = create_dataset_with_full_coverage(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "col_name = \"Age\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "explainer = shap.TreeExplainer(model_xgb)\n",
    "data_copy = res_df.copy()\n",
    "shap_values = explainer(data_copy).values\n",
    "mean_importance = list(np.mean(np.absolute(shap_values), axis=0))\n",
    "max_list = sorted(mean_importance)[-5:]\n",
    "cols = []\n",
    "indexes = []\n",
    "for max_val in max_list:\n",
    "    indexes.append(mean_importance.index(max_val))\n",
    "    cols.append(data.columns[mean_importance.index(max_val)])\n",
    "col_vals = list(set(data[col_name].values))\n",
    "col_vals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': 'Центрированный график изменения важности переменных'}, xlabel='Age', ylabel='Важность переменных'>"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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fdQAAAAB8KysrS//617+Umpqq+Ph4r/tZTmBKhfNc3n///Zo2bZqrrWbNmkpOTtbQoUOtnrZCypLANNO7d281adJEb7/9tul2swrMpKQkHTlypMQXuLJzOBxasmSJBgwY4DEVACou5KPnZN+8xq3NiKmh/IlvBCki36HvwCr6TuVl2/W7Qt/x/GNd/sWTZLQ9MwgR/YN+A6voO7CKvgMr6Dewir4Dq6p630lLS1Pt2rVLTWBWqDwyJCREffr00euvv660tDTZbDb16tVL/fr1q8hpg6JHjx5auXKl1+0RERGKiIjwaA8LC6uSHeRE1eV5VDpxCR5Ntqx0hYXYJXtI4OPxA/oOrKLvVEL5uabNoXnZUiV5r+g3sIq+A6voO7CCfgOr6Duwqqr2nbLGbGkVcklyOp26//77NWzYMGVkZOiGG25Qo0aNNH/+fPXo0UObN2+2euqg2LBhgxo0aBDsMFDdxCR4thlOKTvDsx0Agi0327z9xMXIAAAAACCALFVg7tu3T1dccYVWrlyphIQEJScna9iwYTp27JiuuuoqLVq0SN27d9err76qq6++2tcxe8jIyNC2bdtcP+/YsUMbNmxQzZo11aRJE02ePFl79+7VW2+9JUl64YUX1Lx5c7Vv3145OTmaOXOmvvnmGy1evNjvseIkE1PDvD0z1fs2AAgWbwlM/ugCAAAAIIgsVWB26dJF3333nbp3766ffvpJw4YNk1Q4/+XChQv1+OOPKycnR2PGjNF1113n04DNrFu3Tl26dFGXLl0kSZMmTVKXLl304IMPSpL279+v3bt3u/bPy8vTnXfeqdNOO029e/fWxo0b9fXXX+u8887ze6w4yZSUwASAyiY3y7ydBCYAAACAILJUgXn48GHddttteuaZZ0zHqk+ePFlnn322rrjiCiUnJ2vWrFkVDrQkffr0UUlrESUnJ7v9fM899+iee+7xa0yAJBKYAKqWPIaQAwAAAKh8LCUw582bp5EjR5a4T69evbRhw4aADCEHKi0SmACqEq9DyDMDGwcAAAAAFGNpCHlpycsiderU0ZdffmnlEkD1YLaIjyRlpAQyCgAoGxbxAQAAAFAJWV6FvKxsNpu/LwFUXpHRkt2k0JkKTACVkdc5MKnABAAAABA8loaQr1ixolz79+rVy8plgKrPZiscRp5+1L2dBCaAyigvx7w9Oz2wcQAAAABAMZYSmH369ClzZaXNZlN+fr6VywDVAwlMAFWFtwrM3CzJWSDZQwIbDwAAAADIYgKzV69eDA0HyspsIR8SmAAqI29zYEpSTpYUHRe4WAAAAADgfywlMJcvX+7jMIBqjAQmgKqixARmBglMAAAAAEFhKYFZkjVr1mjXrl1q2rSpzjzzTF+fHqh6Yk0SmHnZkiNXCosIfDwA4E1eCQlMFvIBAAAAECQ+S2Dm5uZq2LBhWrp0qattwIABWrBggcLDw311GaDqMavAlAqrMBPqBjYWAPDGMEquwMzOCFwsAAAAAFCM3VcneuWVV/T1118rMjJSp59+umJiYrRkyRK9/PLLvroEUDWVlMAEgMoiL0eS4X17DglMAAAAAMHhswTmvHnzFBoaqu+//14//PCDNm7cqJiYGM2bN89XlwCqJhKYAKqCkqovJSowAQAAAASNzxKYmzdv1jnnnKOOHTtKkpo3b64LLrhAf/zxh68uAVRNMQnm7SQwAVQmJc1/KUk5zIEJAAAAIDh8Ngdmenq6kpKS3NoaN26szEz+wYOTnLcKzC/+Iy2a7f/rh0VIzTpIg6+XYuL9fz0AVVNuVsnbqcAEAAAAECQ+S2AahqGQkBC3tpCQEBlGCfNpASeDaC9Jw/y8woe/5WZJv30npRySrn/S/9cDUDWVNoScOTABAAAABInPhpAD8CIsXIqIDnYU0t9bpCN7gx0FgMqKOTABAAAAVFKWKjDHjh1r2r5y5Uq3bevWrbMWFVDdNGgh7fwt2FFIR/dJtRsFOwoAlVFpc2CSwAQAAAAQJJYSmMnJyabt27Zt07Zt29zabDablUsA1cs5F0t7NksF+cGN4/jB4F4fQOVV2hyYLOIDAAAAIEgsJTDnzJnj6ziA6q1lF+m6J6U/vpfSjwfmmhu+8WxLORSYawOoehhCDgAAAKCSspTAHD16tK/jAKq/hqcUPgJl6zopK829jQQmAG9KG0JOBSYAAACAIGERH6C6Sqjr2UYCE4A3pVVg5mZJBQWBiQUAAAAAiiGBCVRXJDABlEdpCUyJKkwAAAAAQeHTVcjN2Gw2zZo1y8plAFSEWQIzJ1PKzpSiYgIfD4DKrbRFfCQpJ0OKifd/LAAAAABQjM9WIS9abdwwDI92EphAEJglMKXCKsyo5oGNBUDll5dT+j4s5AMAAAAgCHyyCrlhGBo7dqzOOeccXXfddT4JDEAFJXpLYB6UGpDABHCCMlVgMoQcAAAAQOD5bBXysWPHqmXLlqxQDlQWJVVgAsCJyjIHJhWYAAAAAIKARXyA6ooEJoDyIIEJAAAAoJIigQlUV2ERUkyCZ/txEpgATOSVZRVyEpgAAAAAAo8EJlCdmVVhUoEJ4ESGUcYKTObABAAAABB4JDCB6sxsIZ+UQ4XJCgAokpcjqQyfC1RgAgAAAAgCS4v47N6927Q9IyPDdFuTJk2sXAZARZlVYOZlS9npUnR84OMBUDmVpfpSYg5MAAAAAEFhKYHZrFkz2Ww2tzabzaaPPvpIH330kUd7fn6+9QgBWJdQz7w95TAJTAD/KMv8lxIJTAAAAABBYSmB2aRJE48EJoBKyNtK5McPSg1PCWwsACqv3Kyy7ZfDHJgAAAAAAs9SAnPnzp0+DgOAXyTUMW9nIR8AxTGEHAAAAEAlxiI+QHXmrQKTBCaA4sqawGQRHwAAAABBQAITqM5Cw6S4mp7tJDABFFfWOTDzcqQC5rUGAAAAEFg+XYXcG1YhB4Iooa6Ufsy9jQQmgOLKOgemVDiMPDbBb6EAAAAAwIl8tgq5N6xCDgRZQl1pz2b3tpRDkmFILMYFQCr7EHKpcCEfEpgAAAAAAshnq5Dv2rVLMTExql27tk8CA+AjZvNgOnKlzFSSEAAKlSeByUI+AAAAAALMZ6uQ2+12jRo1SrNnz65oTAB8KbGeeXvKIRKYAAqVdQ5MiYV8AAAAAAQci/gA1R0rkQMoTbnmwMz0XxwAAAAAYIIEJlDdkcAEUBqGkAMAAACoxEhgAtVdfC3JZnKrHz8Y+FgAVE5mCczoePN9GUIOAAAAIMBIYALVXWiYFFfTsz3lcOBjAVA5mc2BWcPLonxUYAIAAAAIMBKYwMkg0WQYOUPIARQxmwMzKk4Kj/Rsz2EOTAAAAACBZWkV8rFjx5q2r1y50mObzWbTrFmzrFwGgK8k1JV2bXJvSzkkOZ2Snb9jACe93BzPtogoKTJWyjthGxWYAAAAAALMUgIzOTnZtH3btm3atm2bWxsJTKASSKjn2VbgkDJTzIeXAzi5mFVgRkRLUbFS2hH3dhKYAAAAAALMUgJzzpw5vo4DgD95W4n8+CESmMDJzun0rLKUpPAoKTLGs51FfAAAAAAEmKUE5ujRo30dBwB/8pbATDkkNWkT2FgAVC6OXEmGZ3tEVGEF5omymQMTAAAAQGAx+R1wMjBbxEdiIR8AUq7JCuSS9wQmFZgAAAAAAsxSBWZxmzZt0urVq3X48GG1b99ew4cPlyQ5nU7l5+crPDy8wkECqKC4WpLNLhlO9/aUg8GJB0DlkectgRlduIjPiRy5Ur5DCg3zb1wAAAAA8D+WKzD37Nmj/v3767TTTtNNN92k//u//9P8+fNd29944w1FRUVp6dKlvogTQEWEhEg16ni2pxwOfCwAKhezBXwkKTxSijKZA1NiIR8AAAAAAWUpgXns2DH17t1b33zzjdq3b6+bb75ZhuE+f9all14qu92uBQsW+CRQABWUYJLAPE4FJnDS8zqE3EsFpsQwcgAAAAABZSmB+dRTT2nnzp266667tHHjRr3yyise+yQmJuq0007TypUrKxwkAB8wW8gn9YjkLAh8LAAqj/LOgSmxkA8AAACAgLKUwPz000/VrFkzPfnkk7LZbF73a9Gihfbt22c5OAA+lFjPs82ZL6UfD3wsACqPkubA9JbApAITAAAAQABZSmDu2rVLXbt2ld1e8uHh4eE6duyYpcAA+JhZBabESuTAya6kOTAjmQMTAAAAQPBZSmBGRkYqPT291P12796tGjVqWLkEAF8jgQnATElzYHodQk4CEwAAAEDgWEpgtmnTRj/99JMyM73PgXXkyBFt3LhRHTt2tBwcAB/ylsBkIR/g5FbSHJheF/FhDkwAAAAAgWMpgXnJJZfo6NGjmjRpkpxOp+k+d999t7KysnTZZZdVKEAAPhKXKNlDPdupwARObmZzYNrsUlgEQ8gBAAAAVAqWEpjjx49Xhw4dNHPmTPXo0UNPPPGEJOmvv/7Sc889p549e+qtt95S586dNWbMGF/GC8Aqe4iUUMeznQQmcHIzmwMzPFKy2aSQkMKh5CdiER8AAAAAAWRSjlW6yMhIffXVVxo1apRWr16tn3/+WZK0cuVKrVy5UoZhqHv37po/f77CwsJ8GjCACkioKx3b795GAhM4uZkNIS+etIyM8UxyZjOEHAAAAEDgWEpgSlKDBg20cuVKffXVV/riiy+0fft2OZ1OJSUlafDgwbrwwgtls9l8GSuAijKrwEw9IhUUFFZaATj5mCYwo/75/6hYKfWw+3YqMAEAAAAEkOUEZpGBAwdq4MCBvogFgL8l1PNsM5xS2lEp0csiPwCqN7M5MIsnMM3mwWQOTAAAAAABZGkOTABVlLeVyBlGDpy8TOfALF6BGee5nQQmAAAAgAAigQmcTLxVWZLABE5euTmebcXnwIwyqcDMYQ5MAAAAAIFjaQh5SDnmyrPZbMrPz7dyGQC+5rUC82Bg4wBQeZhVYLoNIY/13J6fJznypLBw/8UFAAAAAP9jqQLTMAzXf0t7OJ1OnwYMoAJiEqSQMM92KjCBk5PTKeWZVWCesIiPGRbyAQAAABAglhKYdnvhYf3799eCBQvkdDpLfACoJOx28yrM4yQwgZOSI1eS4dkeXsoiPhLzYAIAAAAIGEsJzB07dujOO+/U+vXrdeGFF6pVq1Z68cUXlZHBP2aASs8sgUkFJnByyjVZgVw6oQLTZBEfiQQmAAAAgICxlMBMSkrS008/rb///lvTp09XaGio7rjjDjVs2FC33nqrtmzZ4us4S7RixQoNGzZMDRs2lM1m0/z580s9Zvny5eratasiIiLUsmVLJScn+z1OoFIwW8gn/ZiU7wh8LACCy2z+S6n0RXwkFvIBAAAAEDAVWoU8KipK48aN06ZNm7Ro0SKde+65evXVV9WuXTvdfffdvoqxVJmZmerUqZOmT59epv137NihoUOHqm/fvtqwYYMmTpyo66+/Xl999ZWfIwUqAbMKTMMppR0JfCwAgivPSwVmeOQ//2+2iI9EBSYAAACAgLG0CrmZ888/X71799aUKVP01FNPBbQKc/DgwRo8eHCZ958xY4aaN2+uZ599VpLUtm1brVy5Us8//7wGDhzorzCBysHrSuSHpZoNAhsLgODyOoS8LBWYJDABAAAABIZPEph79+7V9OnTNXPmTB09elRNmjTRpZde6otT+8X333+v/v37u7UNHDhQEydO9HpMbm6ucnNzXT+npaVJkhwOhxyOqjv0tij2qvwcUD622FqmN37+0f0yktqW+Tz0HVhF36k8bFnp5p8HIWEyit6f0EiFmexTkJEmZwDfQ/oNrKLvwCr6Dqyg38Aq+g6squp9p6xxVyiBuXLlSr300kuaP3++8vPzde655+q2227TRRdd5FqpvDI6cOCA6tWr59ZWr149paWlKTs7W1FRUR7HTJ06VQ8//LBH++LFixUdHe3RXtUsWbIk2CEgQMId2TKrV/5r3Wpt3pdT7vPRd2AVfSf4ko5uU1eT9pXrflLqpt2FPxhODZdkO2GfXVt+16+ZC/0coSf6Dayi78Aq+g6soN/AKvoOrKqqfScry8u8/CewlMBMTk7Wyy+/rA0bNig8PFxXXnmlbr/9dnXu3NnK6aqEyZMna9KkSa6f09LSlJSUpPPPP1/x8fFBjKxiHA6HlixZogEDBigszKzGBtWOYcjYMl82R65b86l14tViyJAyn4a+A6voO5WHfd0iafdKj/az+/aXajX8p2HzRx6L9jSrV1tJ5fjMqCj6Dayi78Aq+g6soN/AKvoOrKrqfadohHNpLCUwx44dK5vNpn79+ummm25S3bp1lZaWphUrVpju36tXLyuX8Zv69evr4MGDbm0HDx5UfHy8afWlJEVERCgiIsKjPSwsrEp2kBNVl+eBMkqoKx3e49ZkTz0iu4U+QN+BVfSdSiA/z7Q5LCZeKv7eRMZ6JDDtuVmWPjMqin4Dq+g7sIq+AyvoN7CKvgOrqmrfKWvMFRpC/s033+ibb74pcR+bzab8/PyKXMbnevbsqYUL3Ye9LVmyRD179gxSRECAmSQwlXIoOLEACB6vi/ic8Me8qBgp5YR9TkhoAgAAAIC/WEpg9urVSzbbibNhBU9GRoa2bdvm+nnHjh3asGGDatasqSZNmmjy5Mnau3ev3nrrLUnSuHHj9Morr+iee+7R2LFj9c033+iDDz7QF198EaynAASW2Urk6cekfIcUWvX+YgPAolyT+WZsdinshBEHkbGe+2Wn+ycmAAAAADiBpQTm8uXLfRxGxaxbt059+/Z1/Vw0V+Xo0aOVnJys/fv3a/fu3a7tzZs31xdffKE77rhDL774oho3bqyZM2dq4MCBAY8dCAqzBKZUWIVZu1FgYwEQPHkmFZjhkdKJf6SMMktgUoEJAAAAIDAqNIS8sujTp48Mw/C6PTk52fSYn3/+2Y9RAZVYYj3z9pTDJDCBk4nZEPKIaM82swRmToZkGJ7JTgAAAADwMbsvTpKXl6f9+/fr2LFjvjgdAH/zWoF50LwdQPVkmsA0WcwuMsazrSBfcpgvAgQAAAAAvlShBOY777yjHj16KCYmRo0bN9Zdd93l2vbJJ5/oX//6l3bs2FHhIAH4WEId83YW8gFOLmZDyM0SmGYVmFJhFSYAAAAA+JnlBOb111+v0aNHa926dYqKivIYwt2qVSvNnTtXH330UYWDBOBjUXFSuEmSggQmcHIxW8TH7LPBbBEfScomgQkAAADA/ywlMP/73/9q9uzZ6tChg9auXavU1FSPfdq3b6/GjRvryy+/rHCQAHzMZjMfRn6cBCZwUsnN8WwznQPTZAi5RAITAAAAQEBYSmD+5z//UWxsrD7//HN169ZNNi8T+J922mkMIQcqq0STBCYVmMDJxawC03QIeZz58QwhBwAAABAAlhKYGzdu1BlnnKGkpKQS96tZs6YOHmRREKBSMqvAzEyR8nIDHgqAIHA6pTyzCswyLuIjSdmZvo0JAAAAAExYSmDm5uaqRo0ape53+PBhhYSEWLkEAH/zthJ5KlWYwEnBkSvJ8Gw3mwOTRXwAAAAABJGlBGajRo30xx9/lLiPYRjatGmTmjdvbikwAH6WUM+8nWHkwMkh12QFcsl8DkwW8QEAAAAQRJYSmOedd542b96sTz/91Os+b7/9tv7++28NGDDAcnAA/MhbBSYL+QAnB7P5LyXzIeQRUZJM5rsmgQkAAAAgACwlMO+66y5FREToX//6l1544QXt27fPte3YsWOaMWOGbrnlFsXExOi2227zWbAAfMhbApMKTODkkOetAtMkgWm3m69EnsMcmAAAAAD8z1IC89RTT9Wbb74pp9OpO++8U0lJSbLZbHrzzTdVp04djR8/Xvn5+UpOTlaTJk18HTMAX4iKMV+YI4WFt4CTgrch5GZzYErmw8ipwAQAAAAQAJYSmJI0atQorV27VqNGjVJcXJwMw5BhGIqMjNSwYcP0/fffa+TIkb6MFYCvmVVhphwOfBwAAq88c2BK5gv5sIgPAAAAgAAIrcjBHTp00Ny5c2UYho4ePSqn06natWvLbrecFwUQSAl1pQM73NsYQg6cHMozB6ZkXrGdzRByAAAAAP5XoQRmEZvNptq1a/viVAACyawCMyutsDLLWxIDQPVQnjkwJfMKzOx038UDAAAAAF5UOIGZl5enn376SX///bcMw1Djxo3VrVs3hYeH+yI+AP5U0kI+9ZoGNhYAgVXeOTBNh5BnSoYh2UxWKAcAAAAAH7GcwMzLy9PDDz+sV199VWlpaW7b4uLidPPNN2vKlCmKiIiocJAA/CSxnnk7CUyg+ivvHJhmi/g4C6S8HCq2AQAAAPiVpQRmTk6Ozj//fK1atUqGYahWrVpq1qyZJGnnzp06evSonn76aa1cuVJLlixRZGSkL2MG4CslVWACqN7M5sC02aUwLyMookzmwJQKqzBJYAIAAADwI0ur7Tz11FNauXKlTj31VH322Wc6fPiw1q5dq7Vr1+rw4cP6/PPP1apVK61evVpPP/20r2MG4CskMIGTl9kcmOGR3oeDm1VgSlI2K5EDAAAA8C9LCcx3331XsbGx+uabbzR06FCP7UOGDNHSpUsVHR2t//73vxUOEoCfRERJUXGe7ccPBj4WAIFlNoTc2/BxyXwOTIkEJgAAAAC/s5TA3L17t/r27auGDRt63adhw4bq16+fdu/ebTk4AAFgVoWZcjjwcQAILNMEZglDwb0lMHNIYAIAAADwL0sJzMTEREVFlT7fVWRkpBITE61cAkCgJJolMKnABKo9szkwS0pgMoQcAAAAQJBYSmD2799fK1asUG5urtd9cnJy9N1336lfv36WgwMQAGYVmDmZUnZm4GMBEDimc2CWVIFZwiI+AAAAAOBHlhKYjz32mBwOh/71r3/p0CHPxT6OHDmiq666Sg6HQ48//niFgwTgRwn1zNtTWcgHqNbKOwcmFZgAAAAAgiTUykHJyckaOnSo3nrrLX311Vc6//zz1bx5c0nSjh07tHjxYmVnZ+uaa67Rm2++6XaszWbTv//974pHDsA3vK1EfvyQVL95YGMBEDjlnQMzIkqy2SXD6d5OAhMAAACAn1lKYE6ZMkU2m02SlJWVpfnz55vu99Zbb8kwDEmFiUvDMEhgApWNtwRmChWYQLXldJoPIS8pgWmzFS7kk5Xm3s4iPgAAAAD8zFIC88EHH3QlMAFUcV4TmCzkA1RbDi9zWJc0B6YkRcZ4JjCZLxcAAACAn1muwARQTYRHSDE1pMxU93YqMIHqy2wFcqnkOTClwgrME1GBCQAAAMDPLC3iA6CaMVvIJ+Vw4OMAEBhm819KJQ8hl8wX8mEOTAAAAAB+ZqkCs7jU1FStXbtWhw8fVtOmTXXWWWf5Ii4AgZRQV9q71b3t+EHJMArnvQNQvXitwCwlgRkV49lGAhMAAACAn1muwExPT9f111+vunXrauDAgbrqqqs0c+ZM1/aZM2eqYcOG+uGHH3wSKAA/Sqjj2ZaXTWICqK7ycszbS5sD03QIeWbhHzsAAAAAwE8sJTCzs7PVp08fzZ49W4mJiRo8eLBrtfEiF1xwgQ4ePOh1hXIAlUiiyRByiXkwgerK6hyYZkPIDaf3IekAAAAA4AOWEpjPPfecfv75Z11xxRX666+/9Pnnn3vsU79+fbVt21bLli2rcJAA/MzrSuQkMIFqyeocmGYVmBIL+QAAAADwK0sJzPfff1/169fXrFmzFBNjMh/W/7Rq1Up///235eAABAgJTODkkmd1ER8vv/OzMysWDwAAAACUwFIC86+//lKPHj0UGRlZ4n7R0dE6cuSIpcAABFANkzkwpcKFfABUP94qMK3MgSlRgQkAAADArywlMENCQuRwOErd7++//y6xQhNAJREWLsUmerZTgQlUT16HkJcyB6a3BCYLfgEAAADwI0sJzFNOOUUbN25Ufn6+130yMjL0yy+/qG3btpaDAxBAZgv5kMAEqiezRXxs9sI/ZpTEbBEfiQQmAAAAAL+ylMAcPny49u/fr8cee8zrPo899phSU1N10UUXWQ4OQACZzYOZckgyjMDHAsC/zObAjIiSbLaSj2MIOQAAAIAgsJTAvOOOO9SoUSM9+uijGjFihN59911J0sGDB/Xxxx/r8ssv1zPPPKNmzZpp3LhxPg0YgJ+YJTAduVJWWuBjAeBfZkPIS5v/UmIRHwAAAABBEWrloISEBC1atEjDhw/XggUL9Nlnn8lms2nRokVatGiRDMNQ06ZN9dlnnzEHJlBVeFuJ/PhBKaZGYGMB4F9mCczSViCXpPBIyR4iOQvc26nABAAAAOBHlhKYktSuXTv99ttvSk5O1sKFC7V9+3Y5nU4lJSVp8ODBuvHGGxUdXcpiAAAqD28JzJTDUuNWgY0FgH+ZzYFZlgSmzVY4jDwz1b2dOTABAAAA+JHlBKYkRUZGaty4cQwTB6qDRG8JTBbyAaod0zkwy/hHx0gSmAAAAAACy9IcmACqofjakkwW8Eg5GPBQAPiZ1TkwJSnKZGqYHObABAAAAOA/JDABFAoNk+JrebZTgQlUP1bnwJQKKzBPRAUmAAAAAD+yNIS8RYsWZd7XZrPpr7/+snIZAIGWUFdKO+LedpwEJlCtOJ1ehpCXtQLTJIF5sizi48iTVsyTfl4qhYVLvS+TOvUpnBsUAAAAgN9YSmDu3LmzzPva+FIPVB0JdaXdm9zbUg9LhsE/0IHqwpFr3l7WOTDNEpjZmYWJUXs1H9ix5jPpuw//+Xn+S1JGinTORUELCQAAADgZWF7E55JLLtEzzzzjy1gABJvZQj75edLO3zyTFvn5is86Jh3cKYVWaD2wigsNl2o2qP7JE8AXzFYgl8o+B2akyRyYMgqHpZvNj1mdrPvKs+3rt6QataXTzg18PAAAAMBJwnLWITY2Vk2bNvVlLACCLcHLSuRvPujRFCapryRtWeDPiMouOl665E6pRcdgRwJUbmbzX0oVG0IuFQ4jr84JzOzMwop0M/NfkuJqSs3aBzYmAAAA4CRBuRKAf3hLYFYFWWnS+08VzlEHwDtvFZgVWcRHqv4L+Rze431bQb40d2rJ+wAAAACwjAQmgH8k1At2BBWTmyXt/iPYUQCVW16OeXtZh5B7q7LMybQWT1VRWnIyJ1P672NS+vHAxAMAAACcREhgAvhHQp3CuSSrsrSjwY4AqNy8VmCWcREfrxWY6dbiqSrKUl2Zckh693HvSWIAAAAAlpDABPAPm00aepMUHhnsSKzLoPoJKJG/5sDMruYVmId2l22//X9JHz4rFRT4Nx4AAADgJGJ5EZ9t27bprbfeKtO+11xzjdXLAAi0UzpJt79eOBQ713tCIr+gQL9s/EUdO3VUaEhIAAP8H8OQPn3Fs50EJlAyfy7iU52VZ37LreukL2dKQ28s/MMQAAAAgAqxnMBctWqVVq1aVaZ9SWACVUxMvNT2jBJ3MRwO7fk7S6d17COFhQUmrhMtftNz2GpGSlBCAaqMPC8JzLLOgXkyLuKTnSmlH/Ns73BuYcXl0X2e29YtkhLrSmdf5P/4AAAAgGrOUgJz9OjRvo4DAMovNsEzgckCGkDJKjoHZli4FBJauPJ2cdV5ER9v1ZdN20n9rpBm3idlpXluX/KWVKOO1OEc/8YHAAAAVHOWEphz5szxdRwAUH5xNT0TCwwhB0pmNoTcZi9MTJaFzVZYhZmZ4t5enRfxOexl/ss6SYULn/3rASn531J+nuc+n7xY+FnVtJ1/YwQAAACqMRbxAVB1xSZ6tpHABEpmlsCMiCrfXI1m82BW50V8DnmpwKybVPjfxq2kkZMkmbyGBfnSe1Olw3/7LTwAAACguiOBCaDqik3wbMvL8b5ICQDzOTDLOv9lEbMEZnVexMdsCHlMghQd/8/Pbc+QBl9nfnxOhvTfR5mjFwAAALCIBCaAqivOpAJTIkkAlMRsDsyyrkBeJDLGs606V2CaJTCLqi+LO2Oo1HO4+TlSDknvPl74RxYAAAAA5UICE0DVZTaEXGIYOVASb0PIy+NkqsD0tgJ5HZMEpiQNGC217Wm+bd826cPnJGeB7+IDAAAATgKWFvEBgEqBBCZQfqYJzDKuQF4k0iyBmVWYmLOHWIursippAR8zdrt08e3SW8elPZs9t29dK82+X4qp4bsYS2KzS03aSp36BO6aAAAAgI+RwARQdZnNgSlJ6SQwAa/8NQemjMIkZnScpbAqLbPh45JUt4n3Y8IipMsnS7Puk47t99z+91bfxFZWm3+QflspXftYYWwAAABAFcMQcgBVF3NgAuXnizkwo0zmwJSq5zBybyuQ12lc8nEx8dKV/3Zf6CeY9m2TflwY7CgAAAAAS0hgAqi6IqKl0HDPdoaQA+acTvNFZMq9iI9ZBaaq50I+ZVmB3JtaDaQr7jf/nAqGtV8x/yYAAACqJBKYAKoum818HkwSmIA5R655e3nnwDQdQq5qWoFpMgem2Qrk3iS1lkbeUTgXZbClHJT+/CnYUQAAAADl5tM5MJctW6annnpKu3btUrNmzXTfffepd+/evrwEALiLTSj8R3lxzIEJmDMbPi6Vfw7MSC9DyKtbBWZ2hvkfROqUMP+lmbZnSlc/JK38WDp2wDexlcaZL6Ud9Wxf+6XUuntgYgAAAAB8xGcJzPXr12vQoEFyOBySpC1btmjZsmVavXq1unbt6qvLAIA7s3kwmQMTMGe2ArlkYQ5MLwv1ZKeX7zyVndcFfMpRgVmkRcfCR6A4C6QXb5ZSD7u3b/tZOrpPqtUwcLEAAAAAFeSz8UzPPvusHA6HrrrqKn3xxRe65ZZblJeXp2effdZXlwAAT2ZDyDNTpQLmeQM8eKvA9NkiPtWsAtPrAj4WEpiBZg+RTh9ovm3tosDGAgAAAFSQzxKYq1atUtOmTfXmm29q8ODBeuWVV9S1a1etXLnSV5cAAE9mCUwZUlZqwEMBKj2zBXyk8s+B6XURn2o2B+Zhk/kvpaqRwJSkrv2lkDDP9p+Xeu8LAAAAQCXkswTmwYMH1a1bN9lsNlfbmWeeqYMHD5ZwlO9Mnz5dzZo1U2RkpM444wz9+OOPXvdNTk6WzWZze0RGRgYkTgA+ZjaEXGIeTMCMr+bADAs3X1m7ui3ic/hvz7bYRCnayxD6yiamhtThbM/23CzplxWBjwcAAACwyGcJzLy8PMXHx7u1xcXFuebE9Kf3339fkyZN0kMPPaSffvpJnTp10sCBA3Xo0CGvx8THx2v//v2ux65du/weJwA/iE0wb2ceTMCTr+bAlMwX8qlui/iYrUBeVaovi/QYYt7+40LJMAIbCwAAAGCRT1chD5bnnntON9xwg6699lpJ0owZM/TFF19o9uzZuu+++0yPsdlsql+/fiDDBOAPpkPIZb5y8EmkwChQZkGmMgoyVGBUrvlAHfkOHQs9pj25exRWYDK8tZqLtEcqNjRWkbZIt1ELAeHLBGZUrOd9Vp0W8fG6AnkVS2A2OrXwsfdP9/ZDu6Tdf0hN2wUnLgAAAKAcLCUwd+82nxMqIyPDbVtqqv/noMvLy9P69es1efJkV5vdblf//v31/fffez0uIyNDTZs2ldPpVNeuXfXEE0+offv2XvfPzc1Vbm6u6+e0tDRJksPhCEiVqb8UxV6VnwOCo9L0nchYmaXAClKPyhns2E5wyHFIO3N3yrBQ9VSgAmUVZCnDmaH0gnRlFGQUPpwZ//z//35OL0hXltPLUOHKooH0ytZXgh1FUIUoRHEhcYoNiS182Av/62o74ecIW0SFr2nP+1X2Op5JzPzcX6Vj28sXf50c2eR+LiNsjwqO+Wfu64KCAv0V8ZdWp6xWSEiIX65RnO3wHoWYvFbO2lly+uk5+outazOF5P3i0W78MlMFcRcHIaLACnTfQfVB34EV9BtYRd9BWZwadapqhtZ0a6s0/za3qKxx2wwL/5K22+3lqhop8ONqwPv27VOjRo20evVq9ezZ09V+zz336Ntvv9UPP/zgccz333+vP//8Ux07dlRqaqqmTZumFStW6Pfff1fjxo1NrzNlyhQ9/PDDHu3vvvuuoqPLufgBAJ+xGU4N2/CWTvxE2l67jX5NOjMoMRVnyNC2yG1aE7dGOyJ3BDscAAAAAEAVdemRS9Umu02ww/CprKws/etf/1JqaqrH1JTFWarAbNKkSeCHvflQz5493ZKdZ511ltq2bavXX39djz76qOkxkydP1qRJk1w/p6WlKSkpSeeff36JL3Bl53A4tGTJEg0YMEBhYSffUE5YV6n6zp+fSpnuFd/NasUraYiXud8CINeZq69SvtJ7R97TjlwSlwAAAACAiunatav61Ojj1lap/m1uQdEI59JYSmDu3LnTymF+Ubt2bYWEhHisdn7w4MEyz3EZFhamLl26aNu2bV73iYiIUESE5/C9sLCwKtlBTlRdngcCr1L0ndhEjwSmPStN9iDEdTz/uD48/KE+OPyBjuUfC/j1AQAAAADVU0hoiNd/f1eKf5tbUNaYfbYKebCEh4erW7duWrp0qavN6XRq6dKlblWWJSkoKNCvv/6qBg0a+CtMAP5ktpBPemAX8dmVs0tP7H5CQ38dqhn7Z5C8BAAAAADARyxVYD7yyCPq3Lmzhg8f7ut4LJk0aZJGjx6t008/XT169NALL7ygzMxM16rk11xzjRo1aqSpU6dKKoz/zDPPVMuWLZWSkqJnnnlGu3bt0vXXXx/MpwHAqjiTBGbGcckwJD9Od2EYhn7O+FnvHHpHK1JXyFD5F+epiBCF/LMATLHFXk78b5i9cv0VrqCgQL///rvat29/Uk5QnlWQ5VqIyey/6QXpynHmBDtMAAAAAKg0LCUwp0yZojFjxlSaBOZll12mw4cP68EHH9SBAwfUuXNnLVq0SPXq1ZNUuGq63f5Psenx48d1ww036MCBA0pMTFS3bt20evVqtWvXLlhPAUBFxCZ4tuXnSblZUmSMzy+Xb+Rr6fGleufQO9qUtanMx9UNq6vL6lymVlGtyn9RmxRtj3ZLTkbZo6rkfMQOh0PRGdEaUmtIlRziEAgOw1G4qnx+4cryaflpchg+WFVw0Szp6H73ttqNpIHXlv9cO36VVn/q2T7w2sJz+lhBfoHWrlur7qd3V0ionxPfh3ZLS97ybO8xRDq1q3+v7U+GIX0+Q0o76rlt8A1SzXqBjykAAtp3UK3Qd2AF/QZW0XdQFm2iq9cCPuVhKYFZGU2YMEETJkww3bZ8+XK3n59//nk9//zzAYgKQECYDSGXpIwU0wRmvpGvOQfmaHnKcqUXpJf7chkFGUotSC19x/9pHdVaV9W9SgMSB1S6akhUTmG2MCWGJiox1EvfturQXOlolHtbXBOpxjnlP1d0pHRosWe70Uqq0cVafCVwOBxKyUnRWfFn+T/x/edX0qEoz/Y6/aUaVfyPnaemS1++4dn+6wFp+MjAxxMAAe07qFboO7CCfgOr6DtAyapNAhPAScxsCLlUOA/mCZVgTsOp/9vxf1qSssTvYZ0df7auqneVusd2r5KVkqiG8rI928JNEnVlERlr3p6dYe18lcmhPebtdZsENg5/6NRHWvq2lHfCNAW/rJAGjJaivLyvAAAAQBCRwARQ9XmtwPRcyOeN/W/4NXkZbgvXkJpDdGXdK9UiqoXfrgNYkpvl2RZhMYHpLdFVHRKYh3d7tsUmVo/kXmR0YRJz7SL39vw86eel0lkXBiUsAAAAoCSWE5iLFi1Sv379St3PZrO5rRAOAD5XxgTm4uOL9Z8D//FLCAmhCRpVe5RG1RmlWmG1/HINoEKcTs+qO8n3Ccyc6pDA/NuzrTpUXxbpPsQzgSlJa7+UzhwmFZs3HAAAAKgMLCcwDxw4oAMHDpS6H8MmAfid2SI+UuEcmP+zKXOTpuyc4vNLN41oqivrXqmhtYYq0h7p8/MDPmOWvJSkiGhr5/O2QFZ2prXzVRZZ6abV26rTOPCx+EvdJKn5aYULMRV3/KC07WepVbfgxAUAAAB4YTmBOWjQIN17772+jAUArImIksIjPRM06YVJiEN5hzRp+yTlGrkeh4YoRGfXOFs2le+PLbXDauuc+HN0To1zZLdRrYQqwGz+S8n6HJihYVJYhOQ44b6q6hWYh73Mf1mnGlVgSlL3wZ4JTKmwCpMEJgAAACoZywnM+vXrq3fv3r6MBQCsi02Uju13b8s4rmxntu7cfqcOOw6bHnZfk/t0ce2LAxAgEGRm819K1oeQS4UL+ZyYwKzqc2AeMpn/UiqsWqxOWveQ4mtJaUfd2//8STq6X6rVIDhxAQAAACYoGwJQPZjMg2lkHNMjux7RpqxNpodcXudykpc4eeR6qcCsSALTbB7MnCo+hNxs/ktJqlPNEpghIdLpg0w2GNI6k/kxAQAAgCAigQmgejCZB/ONOlu1+Phi0917xvfUHY3v8HNQQCXiNYFpcQ5MSYoymQezqldgVucVyE/Utb8UYjIY5+elUp7nlBsAAABAsJDABFA9xLlXYC5pkKXXTzEfNt40oqmmNpuqUJvlWTSAqsfXc2BKhUPIT1TVE5iHTObArE4rkBcXmyC1P9uzPSdT+nVFwMMBAAAAvLH0r/cdO3YoNrYaViIAqLqKDSH/o0aeHupy1HS3+JB4vXDKC4r7//buO77K8v7/+Puc7L1IQgIkEPZGQBBBUcGFgntVK2qr/bb6U0trq22trR2OLm1rnVXq3lsciAgOVECRvacQIITseZJz//44Ejk59wnJyTnnPuP1fDzOA3LdJ+d8gpcnJ+9c1/WJTQtWZUBoCMQZmKZbyMM4wKyvluoqPccjbfv44SbMkFYu8hz/Yp5rhaataw3OAAAAgEDwaQVmcXGxcnJy/F0LAPju2wCzLKFVcyaUqSnW8LhLjGJ0V8ldKkqM0NVUQEeaGs3Hu9XEx2QLeXOj1Nri+2Naydv5l5HWwOdwvQZKBf09x/dtl3atD3o5AAAAgBmfAsxnnnlGJSUleucd74e8v/POOyopKdGLL77oc3EA0GmpmWq0O/WzCWXan9Rqepeb+tykCWkTglwYECK8rsDszhmYXlYyh2sjH28dyCN5BabN5lqFaeaLecGtBQAAAPDC5wCzsrJSJ510ktf7nHjiiaqoqNBTTz3lc3EA0FlGaqZuH3NQa7KaTa9fmHuhLsi9IMhVASEkEGdgmjXxkcL3HMwyk/MvJSk3wldtj5hsHkavXSLVHAx+PQAAAEA7PgWYK1eu1KhRoxQfH+/1PgkJCRo9erS+/vprn4sDgM56tPltvdvbfIXZxLSJ+lnvnwW5IiDEmK3AtNmlOO/fy4/IrImPFL4BptkKzLRs70FtpIhLcJ132Z6zVVo+P/j1AAAAAO34FGDu3btXvXr1OuL9evXqpb179/ryFADQaR9UfKD/lD9qeq24JUN39ruTjuOA2RmYCUnda9LiLdgL10Y+ZmdgRvL28cONP02SyVxY9m74nmkKAACAiOFTgJmcnKzycvMOv4crLy/vcJUmAHTX+vr1unXHrabX0ppt+sfOY5Qemx7kqoAQZLaFvDvnX0odrMAMwzMwo7ED+eGy8qTB4z3HayukdZ8Hvx4AAADgMD4tSRo+fLg++eQTHTx4UNnZ2ab3OXjwoD7++GONGDGiWwUC/mYYhtY3rNd7B9/T9qbtchpOq0sKS4ZhaH+P/VqwfYFs3VnB1U3r6tep0em5sizGKd21LFfFCawcAiSZbyGPT+zeYyZ5CTDDcQXmfi/nX0ZyB/L2jp4hbVjqOf7lfNc5mQAAAIBFfAowzzvvPH3yySe67LLL9OKLLyo52X0FR0NDg77//e+roaFB559/vl8KBbqrsqVSbx98W6+Xv66NDRutLicyJEmbajZZXYWpn6/O0sQDiVJGpdWlAKGhKQArML0FmOF4Bma0NvA5XMkoKadQKt/jPr5tlVR9UEo3/6U1rOdsbZWjpkbNVVVqrq5W07d/OqqrJbtdGSUlyhw0SPHp7EgAAADhyacA80c/+pEefvhhvfvuuxo0aJC+973vaciQIZKk9evX65lnntGePXs0ePBg/eQnP/FrwUBXtBqt+rzmc7124DUtqlokh+GwuiQEwXnbU3XB9m+DldoKyTC6d84fEAlMA8xudCCXpMQI6kLuNcCMohWYdrt01HTp/cfdxw2nWr9coOYRJ6q1sVEtjY1qbWhQS0PDd39vbHRda2iQ0xFa32tbW1vVvGGD1pWWKiYmxupyfGMYctTVqbm62iOkbAsqOyG5oEBZgwcrc/Dgtj/Tiopks/t0qhQAAEDQ+BRgJiUl6d1339U555yj5cuX629/+5vbdcMwdNRRR+mVV17xWJ2J0Lb2kUe0/oknrC6j2ypynPp6gkMrj25RTZZhdTkIoqPLEnTTqizZDjWjaG3RG9NPlKOFeRBKmpqa9Mbf/251GVHl9GMzlZTgHlJ8s+QLffHI1G497szjshQX6/4Lgh2vvKjld/yvW49rJpDzZsqYNOVlxbmNNTQ59fZpMwLyfKEqKcGm0yZlehwNUv3qo3r72t9ZU5SfrH7vPatLsFx9aanqS0u1+8MP28ZikpKUOXCgK9AcNKgt3IxL9bLCGgAAwAI+t+Xt3bu3vvjiC73xxht65513tGPHDklSUVGRTjvtNM2aNcvSc/HgG0d9vRoPHLC6DJ844qVN4+1aPTVGu4aF6QoLdEteuU13LeuhOMP9tcdeX6XG6maLqoI3jTU1VpcQVWLtmR5jTXUN3X7Nb25OU1yse/BndzoC9r0kUPMmLclza21lRff/fcJNo6R9++PVM999dW1WZqIy0uNVxWtpxGltaFD5ypUqX7nSbTy5oECxSd1cpR0kMQkJyhw0SD3GjFGP0aOVMWCA7OG62hYAAJjyOcCUJJvNplmzZmnWrFn+qgcWK0+o1e6B4RU8t8TbtOlou9ZNilFzctdqj2s0lFrByrxwF9MiFWx26qKPEpQxxvMHlsTEGFV1bncdELHi4jy3iDoc3W9i1tzsVEq7zRbx8eG1HTUh3q6kRM+3RNEa1m3bUeMRYEpSv+J0rVgVXYFuNKsvLbW6hC6pWLdO2157TZIUm5ysnJEj1WP0aPUYM0Y5o0YpMSvL4goBAEB3dCvARORZlL9Jb/42weoyAq73eqdGLGrRoKVOxTVZXQ38JSbH/CXNLJgAoklsrHmg2NLijwCz1WMsIT68Vj6lp5t/36uqjs5vELt21+roFqfHvOlblKavVx0Qv/ZDqGupr9e+zz/Xvs8/bxtLKy52BZrf3jIGDpQ9lvcHAACEi259125padGLL76ohQsXavfu3ZKkXr166cQTT9T555+vWN4UIISkVBga/lGrRixuVdY+fvyKRA0NLabjiQSYiHJxXgJMR4ACzHiT1Z6hLCM93nQ8WldgtrQ49c2eWvUtct9Wn5wcp7zcJO0rM2kIhaCwx8YqPiPDdUtPd/t7wuFjh/3ZXF2tyg0bVLFhgyo3bFDlxo1qqa+3+ksJupodO1SzY4e2vf66JCk2KUnZI0Z02Jnd6XSqcd8+fTJ/vuwWNzqKTU5W7pgxKjrtNCVkZlpaCwAAVvD5p/oVK1bo/PPP17Zt22QY7mHQI488oltvvVUvvPCCxowZ090aEUSJOdlWl+BXdqdNI77poWO2FmhIaZZiDLs01uqqIoPhdGrPnj0qLCwMme6ldjklbfMYzx86QPX9ewS/IJgKxbkT6ZJszZI8u2ynDRqqopKMbj12XMJ+Se7nUiYkxavo9NO79bjtBXLe9I4vk+R5zkTahOOVrPBaTeovdTF1kvZ6jI+bOVkHiqYoJilJsUlJik1MVExiomKTk9v+HhMfL4XQOegtDofef/99TZ8+XbFxcUf+hBAVm5iomKQkn86Yzz3s/bjhdKr2m29coebGjW3hZt033/ix2tDX0tCg/UuXduq+e9auDXA1nbP9jTe0/I471OuEE9TvrLNUMGWK6/83AACigE8B5p49e3TKKafowIEDys/P18UXX6z+/ftLkrZu3apnn31WW7Zs0amnnqoVK1aooKDAr0UjcDL695f2fmh1Gd3WP7G/zso5SzOyZyhrPGceBYLD4dC8efN0zIwZigulHwjvuFRqcl9Z0mvcaPU676cWFYT2QnbuRLJvNkqP/NJjeMD3LtOAUd3rQq5350pLXnMbirEZmnLnHVKs//77BnTezL1V2r7afSwtR5N+9w//Pk84aW2V/naVVO8e7GY27VfmWbOk+PA5bsbhcMiWmqqE7GxecyTZ7HalFRUprahIfU4+uW3cUVuryo0b21Zq1u3dKxlhsGPFMFT7zTeq+bahaKRztrRo1/vva9f77yshM1PFM2ao36xZyh4xggaqAICI5lOAedddd+nAgQP64Q9/qHvvvVdJ7ToU/vnPf9b111+vRx55RHfffbf+8Y8o/gEAQZNiT9Fp2adpVs4sDU8ezpu4aJWW5RFgquagNbUAoaLJy5bfhGTz8a5I8mz2IklqrJNSM7v/+MFQ5rk6VXl9gl9HKImJkUYeJ33+lvt4c6O04QvXNUSUuNRU5Y4dq9yx4blVpbGiQuUrV+rAihU68PXXKl+1KuK3yjdVVmrj009r49NPK72kRP1mzVLfM89UCotHAAARyKcA8+2331ZRUZHuv/9+xcSYdfxN1H/+8x+99957euuttwgww8ipWadqUNIgq8vosszYTA1PGa4ke9KR74zIlpolHdjtPlZbaUkpQMho9hJgxvvhNTMpzXy8oSY8Asy6aqmuynM8N8oDTEkadYJngClJKxcRYCLkJGZlqdfUqeo11bWq3NnaqqrNm1X+9dc68O2tepvnMTORonrrVn19zz36+t57lT9xovrNmqU+06crLsXLL5kAAAgzPgWYu3bt0jnnnGMaXrY9cGysJk2apFdffdXX2mCB/kn91T+pv9VlAL5LNTkyoLYi+HUAoaT9quRDEvwQYCZ6+eG4oa77jx0MZTvNxwkwpcL+Uk4vqbzdL4U2f+X6xVA4BNSIWvaYGGUNHqyswYM14MILJblWLJavWtW2SrP2m29ktHo2IjvEMAw1NDQoycezR/3FcDpVv9fzTFrzOxva99ln2vfZZ1r2hz+oz8knK2vo0JA6lzbSOVtb5Vi7VpsqKmTv4OdloD3mDjqj1/HHK6242OoyLOFTgJmQkKDqas/D7turqalRQkL4nJEEIAKY/UDdWCc5mqQ4Xo8QpZoazcf9EWAmpZqPN9Z2/7GDYb/J9nGJLeSSK/AYPVX64Gn3ccMprf5YOuZMa+oCfJSQmanC445T4XGdW0F86OzdGSFwZnP1jh3a9tpr2v7GG6rbs6dTn9PS0KBtr7/e1nkdwbXiLZMV7EAnMHfQkZSCgqgNMH1q4zls2DAtXLhQu3Z5edMvaefOnVq4cKGGDx/uc3EA0GVpXpo21ZpsEQWihdcVmH44A9PrCswwCTBZgdmxkV6aPK1cFNw6gCiXXlys0ddfr1nvvqtpc+eq5NxzFcv2cABAFPEpwLz88svV0NCg6dOna968eR7X33zzTZ188slqbGzU5Zdf3u0iAaDTzLaQS1ItjXwQxQJ6Bqa3FZjhsoX8G8+xtBzvwWy0ycqTioZ5ju/ZbP5vByCgbHa78o8+Wsf84Q86d9EiHXv33SqYMkU2u08/1gEAEDZ82kJ+9dVX66WXXtKCBQs0c+ZMZWdnq1+/fpKkbdu26eDBgzIMQ9OnT9fVV1/t14IBoENeA8zKoJYBhBSzFZg2uxQX3/3H9hZghssKzP0mKzDZPu5u9FRp51rP8ZWLpGmXBr8eAJKk2KQk9T3jDPU94wzV79+vHW+9pa2vvaaqTZusLg0AAL/z6Vd1MTExeuutt/SLX/xCKSkpKi8v17Jly7Rs2TKVl5crJSVFv/zlL/Xmm2/Kzm8DAQSTt6YSNTTyQRQzOwMzIck/TR3CeQt5XZVUb3KmN9vH3Q07Voox+Z33ykWS0xn8egB4SM7L09Arr9SMV17R6S++qMGXX67EnByrywIAwG98WoEpSfHx8brzzjv1+9//XsuWLdPu3a4Olb169dL48eNp3gPAGl7PwCTARBQzW4Hpj/MvJcke43qs9s8RDk18vDbwKQpuHaEuKVUadLS0bon7eFWZtGu9VGyyxRyAJWw2m7KGDtW4oUM19he/UOOBA3I6HFaXFVUcDocWLlyoE0880fLmTwgvzB10RkKWl593o4DPAeYhCQkJmjx5sj9qAYDuS0qT7LGSs8V9nAAT0czsDMz4RP89fmKKZ4DZEAZnYJZ5CTBzewe3jnAweqpngClJX39IgAmEKJvNpqTcXKvLiDoOh0P2rCylFBYSQqFLmDtAx9jfDSCy2Gzm28g5AxPRrMkkwPTXCkzJ/BzMcFiBSQfyzhsw1vy/85pPJEdz8OsBAABAVPFpBebjjz/epfvTiRxAUKVlSdUH3Mc4AxPRzDTA9EMH8kPMgq1wOAPTbAt5Oh3ITcXGScOnSMvecR9vqpc2LZeGTbKmLgAAAEQFnwLMK664QrYuHPxPgAkgqFiBCbgzPQPTjwFmYpgGmGZbyHM5/9KrUVM9A0zJtY2cABMAAAAB5FOAefnll3sEmP/73//Uv39/TZkyxS+FAYDPUk0ONq6rdHXLtXNyBqKQ6RmY/lyBabJisTHEz8D02oGc8y+96jNYyuopVex1H9/0pevfMjndmroAAAAQ8XwKMOfOnesx9r///U/HHXecHn300e7WBADdYxZgOltdP2Cbrc4EIpnTKTU3eo778wxMsxWYLc2usxHj4v33PP5EB/Kus9lcqzAXPec+7myR1nwqHX2aNXUBAAAg4nW7CzkAc+Xl0qZN7retW6VGkxwhHBlGrGpqTtCtt8aqCydKBMX5fbN061Em46dXalN1ZtDrgbtQnjuRKCW2UZ/O9Bx/6H9Juu9m/zzHVYNSdcNwz/Fpk2t1oCnbL8/h73lzUb+d+tUYz/HL5vTRKo7M9aoo5Xi9ccpzHuNfPb5IV/wwNANMXnPgq1CaO/Hx0sCB0ogR0siRrj+Li9lYAgCIHgSYQDdUVnqGlIduFRH/A7BNUobVRZjq25QpmQSYNaUVWrmlb7DLgYfQnTuRqFeayfmXkjbtStLKlf55jrVxqZJJgFm6rU7rDvgnwPT3vPlRb/MVmK8v6aMammp7tVKFWjJ0kCb12eg2flTOetXuKtXWigKLKusIrznwVWjNnWXL3D9OTZWGD/8u0Dz0Z16eNfUBABBIBJhws3KltHZt9x7D6ZSam10rDZuavvvz8L97u2YY/vk6Aq2pybWa8sCBI98XwVdaY7KFXFLP1IhPlQEPaQkm519Kqmny3xmYFQ0mW8glZSfV+O05/G1YrmeAuasqRzXNftxaH6GeXDXVI8CUpMtGLdbtiy6yoCIgOtXWSp9/7rodLi/vu0Bz+HApneNpg6qlxaYVKwpVW2tTLD9towuYO+iMyZOl3lF6ZDv/W8DNCy9If/yj1VUA3bO31jzALCDARBRK9xJgVjf5L6iraDRp4iMpKyl0G/kMNwkw15Rx/mVnPLd6iu459VHFxbS6jV82cpFuX3ShXKvWAFhl/37pgw9cN1ghVtLRVheBsMTcwZG9/HL0BpicmgIg4uyryzQd75laGdQ6gFCQFu9lBWZz4FdgZiXW+u05/Ck3uVK5KZ4dyNeU9bGgmvBT3pCueZvGeowPzCnVxF6eKzMBAACA7vJpBWZJSYnHmM1m04svvqgPP/zQY3zLli0+FQdEkqQkacAAqUcPWX4QvD8YhlMHDpSrR48c2Wyh9ruQOFU7UpUe5x6ejO5foZNOsqgktAntuRN5js0xDzAHDE/SSX767W1BovkKzLln/0uPnnVfpx+nvjVJ62sGalX1UK2qGqb1NQPlMFxdzP05b8ZkmJ9/6czpw2tEJ60wpuosLfUYv2XGh/rnlsEWVOQdrznwVSjNnbIyaf16yeGwtAwAACzjU4C5fft20/Ha2lrV1roHBrZISGqATkpIkPr3d3WJbH8rLIysTpEOR6vmzftUM2bMUFxcCH5h92VJZe6vRyeOr9CJnc9SECAhP3cizYp66VXP4X/8O0kq9NNzNKRJd3kO222G7LZWzwtepNtrNSH7K03I/so1EBMrFQ6Qioeppddgvbtuh06ZeXb3580Xu6R5nsM33dVHN7EIs3McR0t/TZaa3JtEndX/E531n6uk2DiLCvPEa04UMIyA/HY41OaOwyFt3CitXi2tWuX6c/Vq17ns4XKOPAAAvvIpwNy2bZu/60AUiY2VEhNdYV9CgvvfY2Ksrq5zbDapZ0/PkLJ37/D5GiJeapZU1m6VVQ1nYCIKNTWajyf4sVlNQrJc5x76+Sfo1hZp13pp13rFSpohm7TvU6l4uFQ8VCoaJqWZn3nbof3mKzCVS3rZaXHx0rBjpa/edx9vqJE2fyUNmWBNXYguKz6QPnhGqq92zceTvy+lZVtdVcDExbma8gwfLl10WL+sujpXE85DoeahP/futa5WAAD8zacAs7i42N91IET86EfSzJndf5z2wWQ4hpQIc2ahRm1l0MsALNduhVybBP+dgSm7XSookUoDe2SMTYa0b7vr9sVbrsHsAqnPYCnBfBu7qU3LPcfSe0iJdCDvktFTPQNMSVq5iAATgffNRunVf3338coPpc1fSufcIA30PKM1kqWkSEcf7bodrqxM2rFDau38Qnj4QUtLiz799FMde+yxiqWVNLqAuYPOGDjQ6gqsE/D/KwzDYBt5GOndO3o7WiHCpJoEmM0NUnOjFJ8Y/HoAqzSbn4Hp1wBTkqZeID3/F8kZ5J+UD5a6bt2Vx+rLLisaJmXkSlVl7uMblkoNdVJSF0JloKuWvu05Vl8tPfUHafI50knfcx1DEcVyc103BJfDYejAgQpNmGAoLnRO00AYYO4AHfPpu/rWrVtNG/m0t3HjRs2ePVtLlizx5WkAwHepmebjNRVSTkFQSwEsZbYC02aXYuP9+zxDJkpX3+0Kr+qquv75hiHt3ynt3iS1WtClgu3jXWe3SyOPlz5+yX281SGt/VQad7I1deE7zU2us3vsEbb9xTCkLV97v/7JK9L2NdL5c6Ss/ODVBQAAAsanAPP444/X+++/ryFDhni9z7333qtf//rXamjwsvIDAALJ2xlYtQSYiDJmZ2AmJAWk4YUKSly37nA0S3s2SzvXSTvWSLs2eN8G70/5HI/jk1FTPQNMybWNnAAz8JoapMr9rlWwlfsPu337cX216xcWI6ZIZ/6f/1deW2X/Ttf3847s3ig9MEea9RNp+OTg1AUAAALGpwBz3759mjp1qt555x0dddRRbte2b9+uK6+8UosXL1Z2drYee+wxvxQKAF3ibQXmkX7gASKNWfjnzwY+/hYXLxUPc92OO8+1JX3fDrVuW6W9XyxUoaNSNl9WeHYkIVkacox/HzNa5PX59vzTre7jO9ZIX38YEkd22FpaVVC5Q7b1X0ixYboS0TBcOwjcQsr9rqZJR/xcp7Rqsev74qlXBrzUoNj8Vefu11QvvfBXaetK6bSrpLiEwNYFAAACxqcA8+WXX9aFF16oadOm6Y033tDkya7faj744IO66aabVFtbq7POOksPPvig8vLy/FowAHSK2RmYEo18EH3MzsAMgVCp0+wxUkGJnD36aFmZNOP00xVXc0DasdZ127nOtfrM8KEDuj1GKuwvnf5DGvh0x6gTPANMSXrl3qCXYiZW0gRJ2rbQ4kos9uX70vTvR8a5kFtWdO3+y9+Tdq2Xzv85590CABCmfHoHM3PmTL311ls666yzdNppp+k///mPnnrqKc2fP18ZGRl6/PHHddlll/m7VgDoPG8BZg0rMBFlmkwCzFBegXkkNpuUU+i6jZ1udTWQpJHHSe/Nda30Q+hqqncF/v1GWl1J9ziaXL+8aK9Hb9cvMsp3m3/e/p3SQz+XZlwtHTUtMMdoAACAgLH7+oknnXSS3n//fcXFxemKK67Q/Pnzdeqpp2r16tWElwCsl5hs3qSELeSINqYBZoScg4fQkJop9R9tdRXojI3LrK6g+3asNW/0NfI46Zq/SGNO8v65Lc3S6/dJL/1dagzC2boAAMBvfA4wJWnixIlatGiRcnNzJUkXXXSRCgsL/VIYAHSLzWZ+DiYBJqKN6RmYBJjws/GnWl0BDklMlXqWmJ/3uGFp8OvxN2/bx/uPcb22nf3/pHNu6PiojNUfSw/OkXZvDkSFAAAgALp9CM7IkSP18ccfa/r06frBD36g2tpaXXvttf6oDQC6JzXL1eTgcJyBiWhjegYmASb8bPAE6ZiZ0mdvWF1J5EtMlTLzpMzcb/88/JYrJaa47vfi36XVH7l/7sFS6cBuqUev4NftL2YNfBJTXefZHjL6BKn3IOnFv5mfzypJFfuk/94ijZ3m9TXR7nRq2O6tsi84KNm7te6j+xJTpIFjXU2zAACIQj4FmCUlnt846+rq5HQ6df311+uvf/2rbN+eK2Oz2bRly5buVQkAvjBbgckZmIgmTqfU3Og5Hs5nYCI02WyuLs9TzpXK91hdjZuWlhYt+ewzTTrmGMXGhnEDm4Rk94DySAaN9wwwJdc28nANMKvLpbJdnuMlo1xNuQ6XUyj94E5p/uPS52+aP56zRVr2rteni5E0UJL2r/a1Yv9a+Ix07o2u7fIAAEQZn97Fbd++3XTcZrPJMAzt2LHDbQwALJFm0sinvlpytnr+oANEIrPwUmILOQInNdP8l0cWMhwOHVy9TUbRUCkuzupygmfAUZLN7tlcaeMy6dizrKmpu7Z8bT7ef4z5eGycdPoPXI2LXvuX1FAbsNKCwnBKC56Uhk+2fkUoAABB5tN3vm3btrnd1q5dq6OOOkqGYei8887T1q1b265t3epl2wYABFpqtueY4ZTqqoJfC2AFs/MvJQJMIBokp0lFQzzHd66TGuqCX48/bDHZPi55DzAPGTJB+r9/SEXD/F5S0FXul+oqra4CAICg82kFZnFxcdvfa2trdeaZZ2rFihW68sor9fDDD8vObwQBhAJvq4BqK6U0k3ATiDRm519KnIEJRItB411duw/nbHU1whkx2ZKSfOZ0mq/A7NHLtbX+SDJ6SLNvlxY/Ly16QZLh9xKDpnI/72MAAFGnW0njwYMHddJJJ2nx4sWSpCFDhrBlHEDoMNtCLnEOJqJHk5cAkzMwgegwaLz5+MYw7EZeulVqqPEcP9Lqy8PFxEgnXuIKMgv6SwrTn1sq9h/5PgAARBifTzLfu3evpk+frnXr1umKK67Q22+/rZtvvlmvv/665s6dq/79+x/5QQAgkFK9BJi1BJiIEl4DTFZgAlGhR28pK9/Vcftwm5aH33nQW1aYj/c/quuP1W+E9KO/uv4NOliI6Whx6O2339bpp5+uuFgLzk+t2i/98yee45UEmACA6OPTCszt27drypQpWrt2re666y49+uijWrNmjc4//3x98sknGj16tP7973/7u1YA6BoCTEQ7zsAEopvNZr4Ks6FW+mZj8OvpDrMAMyZW6jvc98e0x7hWZXq72WNk2OxHvl+gbhl5rkZM7RFgAgCikE8B5pQpU7R9+3Y98MAD+vnPfy5Jys7O1nPPPadnnnlGiYmJuuGGGzRt2jTt3LnTrwUDQKelZMh0exhbyBEtOAMTgNdt5MuCW0d3NDVIu9Z7jhcNleITg19PsMTEuM7ubI8AEwAQhXwKMPfv368nnnhC11xzjce1iy66SGvWrNEZZ5yhhQsXauTIkd0uEgB8EhMjpaR7jtdWBr0UwBKcgQmgeLh5yBdOAeb21a7t3u115fzLcJWZ5zlGgAkAiEI+BZgvvviiLrnkEq/X8/Pz9frrr+vRRx8NWkfy++67T3379lViYqImTpyoL774osP7v/DCCxoyZIgSExM1cuRIzZs3Lyh1Aggys23kbCFHtOAMTACxcebnRO7fGT7NYLyefzkmmFVYwzTALHN1ZQcAIIr4lC7OmjWrU/e74oortHr1al+eokuee+45zZkzR7fddpu+/PJLjR49Wqeeeqr27zd/U/bpp5/qkksu0Q9+8AN99dVXOvvss3X22WcHpVYAQUaAiWjGGZgApPDfRr75K8+xlAwpv2/QSwk6swDT2cJ7GQBA1An48shevXoF+in097//XVdffbWuvPJKDRs2TA888ICSk5P16KOPmt7/3nvv1WmnnaabbrpJQ4cO1R/+8AeNHTuWxkNAJErN9ByrqZCMDtqOApHC7AxMm12KjQ9+LQCsM3CsTM+EDocAs2KfdLDUc7xktBSknV6WMgswJbaRAwCiTqzVBXRXc3Ozli9frltuuaVtzG63a/r06VqyZInp5yxZskRz5sxxGzv11FP16quven2epqYmNTU1tX1cXV0tSXI4HHI4HN34Cqx1qPZw/hpgjXCZO/aUDMW0H2xplqOumnMALRIucycSxDTUefym0khIUktLiyX1dAfzBr5i7khKSFFM4QDZ92xyGza2r1JLXU1IN8Kxb1zu+X1cUku/UTIC/N80FOaOLS3b9Ae2lvJSGQUDgl4Pjsx03jTVy77kddlKt5if5xqKElPlHHy0jBHHWV1J1AiF1xyEp3CfO52tu1sB5ksvvaQXXnhBGzZsUHV1tQyTFU02m01btmzpztN06MCBA2ptbVV+fr7beH5+vtavN+lWKGnv3r2m99+7d6/X57njjjv0+9//3mP8vffeU3Jy+Icg8+fPt7oEhKlQnzsl+/fIrJXY4rdeU21iRtDrwXdCfe5Eggm7tqug3VhDqzQ/jM99Zt7AV9E+dwYZqRrabszW2qIvX3xMezOLLampM47eulCFJuPvb92vpl3BeS2zcu4kNtfpVJPxTV98rI07a4JeDzqvbd4YhqZueFOZDeXWFuQD+/rPtHrpEm3JH2F1KVEl2r9fwXfhOnfq670ce9WOTwGmYRi68MIL9fLLL5uGlpIruDQMQzabyXaVMHTLLbe4rdqsrq5Wnz59dMoppyg93aTLcZhwOByaP3++Tj75ZMXFxVldDsJIuMwd29pM6RXPpl5Tx42WUTws+AUhbOZOJIh5cqlU5T6WlJmtGTNmWFNQNzBv4Cvmzrf2DZMe8TxL8uh0m1pD9TXB2arYvz/vMWzkFWvaWecH/OlDYu44nTLWvSxbu1V7g/OyNCBU/7tFufbzxrZ9tWJXhF94ecjwmq0afMVNUoT8XB/KQuI1B2Ep3OfOoR3OR+JTgPnwww/rpZde0ujRo3X33XfrwQcf1CuvvKL169dr06ZNeuKJJ/T888/rN7/5jX7wgx/48hSd1qNHD8XExGjfvn1u4/v27VPPnj1NP6dnz55dur8kJSQkKCEhwWM8Li4uLCdIe5HydSD4Qn7uZOaaDsc2VEuhXHcUCPm5EwkcTR5DtoTksP53Z97AV1E/d3oNkNJzpGr3IMW++UvZY2JC8zzJnVtMm5HZBhwV1P+Wls+djB6us0APY68+IHs0z+cw0DZv9u+wupRusdUcVJzTISWmWF1K1LD8NQdhK1znTmdr9umdyhNPPKGEhAS9/fbbOvnkk5WWliZJGjhwoGbMmKFnnnlG999/v/785z8HdPu4JMXHx2vcuHFasGBB25jT6dSCBQs0adIk08+ZNGmS2/0l11Jbb/cHEMbMmvhIdO9EdDDrQs7Zr0B0stnMu5HXVUqlgX2/7rMtK8zH+48JZhXWM2vkQxOf8GHWhCrc1HVudRQABJJPKzBXr16tSZMmta1YPLRN/PAt49dcc43uuece/eUvf9FJJ53kp3LNzZkzR7Nnz9b48eM1YcIE3XPPPaqrq9OVV14pSbr88svVq1cv3XHHHZKkG264QVOnTtXf/vY3nXHGGXr22We1bNkyPfTQQwGtE4AFUrPMx2srg1oGYIkmky7kCaHbrANAgA0aLy1713N84zKp18Dg13MkZgFmbLxU1P40zwhnFmBWHXA1g7GbtThCSDlo0mchJk7q0Sv4tRxJY51UVeY5Xlcl5bQ/VRsAgsunALOhoUEFBd+9gB3aWl1dXa2MjO+aYowZMyYoh4hedNFFKisr029/+1vt3btXY8aM0TvvvNPWqGfnzp2yH7Yt5thjj9XTTz+t3/zmN/rVr36lgQMH6tVXX9WIERxODESchCQpLlFyNLqP17ACE1Gg2SzAZAUmELX6jXQFgC3N7uMbl0knXmJNTd401Eq7N3mO9x0uxcUHvx4rZeZ7jjlbXO9lMnoEvx50jdkKzKIh0uzbg1/LkWz5Wnrid57jdVWeYwAQZD4FmPn5+Sor++43M3l5rt8Kbt68WePGjWsbP3jwoBobGz0+PxCuu+46XXfddabXPvzwQ4+xCy64QBdccEGAqwIQEtKyPN88soUckc7plJpNvgfHJwW/FgChIS5BKhnlCiwPV7rVdTZmeo41dZnZtkoynJ7j0bZ9XPJ6nrcq9xNghjpHs2u1bHvZIbqaMSXDfLyeABOA9Xw6A3PAgAHaunVr28dHH320DMPQAw880Da2bt06ffjhh+rfv3/3qwSA7jA7B5MAE5HOLLyUXKuSAUSvQUebj29aHtw6jmSzZ8d0SVL/o4JbRygw20IucQ5mOKjcJ8nwHA/ZADPdfJwVmABCgE8B5imnnKJt27Zp7dq1bR/36dNHjz76qI4++midd955mjRpkhwOhy6//HK/FgwAXWZ2DiZnYCLSmTXwkQgwgWg3aJz5+IZl5uNWMAzz8y/TcqTc3kEvx3IEmOHL7PxLKXQDzGQCTAChy6ct5JdccolaWlrU0OA6Wys+Pl7PPfeczj77bC1fvlzLl7t+g3vWWWfphhtu8F+1AOALswCzvlpqcUixccGvBwgGs/MvJbaQA9EuPUfqWSLt3eo+vvVrydHk2mZutfI95o1E+o92dVOPNmnZkj3Wde7l4Sr2WVMPOq98j/l4ds/g1tFZMbFSUqrrDNrD0YUcQAjwKcAsKirSr3/9a7exY445Rtu2bdPixYt18OBBDR06VGPGjPFHjQDQPWleOpHXVXF2FCKXWQdyiSY+AFyrMNsHmC3N0rbV3ldoBpPZ6ktJGhCF28clyW53nYPZ/jxvVmCGPq8rMEM0wJSk5AyTAJMVmACs51OA6U1SUpJOPfVUfz4kAHSf2RmYkuscTAJMRCqvASYrMIGoN2i8tPgFz/GNy0I4wLS5GhBFq8w8kwDTZJUqQotZB/L0nNBY6exNSoZUvtt9jAATQAjw6QxMAAgrZlvIJc7BRGTjDEwA3hQOMO82vHGZ6/xJK7U4XCtB2yvs7/18vmhg1om8+oDU2hr8WtB5ZgFmqJ5/eYjZa0M9W8gBWM9vKzDr6ur0+OOPa926dUpNTdWsWbN0zDHH+OvhAcB33gLMGjqRI4JxBiYAb+x2aeA4acUH7uPVB6R9O6SefS0pS5K0a4PkaPQc7z8m6KWEFLNGPs5WqeagebgJ67U4pKoDnuMhH2Ca/KKgrkpyOl2vHQBgkU6/Am3fvl0XXnihcnJylJGRocmTJ+vtt9+WJG3evFmDBw/Wddddp/vuu0933XWXJk+erNtuuy1ghQNAp3k7A7OWABMRjDMwAXRk8NHm4xuXBreO9rydfxn1AWa++XgljXxCVlWZZDg9x0M+wDRZgWk4pcZaz3EACKJOBZgHDx7UlClT9NJLL6miokI1NTVasmSJzj77bC1ZskSXXnqp9uzZo5ycHI0bN045OTkyDEN//OMftXjx4kB/DQDQseQ0yWbyckeAiUjGGZgAOlIy2tVxuL2Ny4Jfy+G2fOU5Fp8o9Rkc/FpCidkKTIlGPiHMZrZ9XArtBj6SeYAp0YkcgOU6FWD+9a9/1Z49ezRw4EA9/PDDeuONN/Sb3/xGdrtdP/3pT7Vs2TL96le/0r59+/TFF19o3759uvnmm2UYhh588MFAfw0A0DF7jPmbMbaQI5JxBiaAjiQkSX1HeI5/s8m6M6LrqqTSrZ7j/Uaah63RxGuASSOfUGWr8NKBPKcwuIV0VbK3AJNGPgCs1al3AvPmzVNycrIWL16svDzXN88zzjhDeXl5uv7661VUVKTbb79dNptNkmSz2fSHP/xBTz31lJYsWRK46gGgs9KyPFdc0sQHkczsDEybXYqND34tAELToPEmW7YNafOX0piTgl/P1pXm49G+fVySUjNdIW5ri/s4KzBD10EvAWaWl+MAQoXXFZgEmACs1akVmFu2bNGxxx7bFl4ecu6550qSRo0aJXu7A31jYmI0evRolZZ6WToPAMFk1siHLeSIZGZbyBOSpW9/2QgAGjjOfNyqbeSbTbaPS1L/o4JbRyiy26UMk2Y9BJghy3QFZlq260iEUOYtwKQTOQCLdSrArKurU2Gh51L3ggLXAcQ9evQw/bycnBw1Nzd3ozwA8BNvAaZhBL8WIBhMA0y2jwM4THZPKbeP5/jmFa4OysFkGOYNfDLzQ//MwGAxW7lXQROfUGUzW4EZ6g18JPMu5BIrMAFYrtNdyNuvsJTUtmUcAEJeaqbnWGuL1EBHRUQoszMwCTABtDdovOdYc4O0Y21w69i/03xnxIAxrBw/xOwczOpyqbU1+LWgQzZnq1Rlsjo2HALMpFTz5pd1lUEvBQAO1+kAEwDCWprJCkyJczARuZobPcfiCTABtGMWYErB30ZutvpS4vzLw5kFmIbTFWIipCQ318lmtssnHFYT22Ok5DTPcbqQA7BYp9v5bd68WY8//niXrm3evNn3ygDAn8y2kEuu1R55JtvnENkMQyrbJZXv8e3zY+OkwoHet1mFAtMVmMnBrwNAaOs92LXiqv2OhI1LpdOuCt7qR7MA02Z3dSCHi9dO5PulLC/XYImUJi9hX6h3ID8kOd1zyzhbyAFYrNMB5ieffKJPPvnEY9xms3m9ZhgG28wBhIaOAkxEF2er9NI/pDWe37e6JCZOOutaadRU/9Tlb6ZnYIZ44wAAwRcTIw0YK61a7D5esU86sFvK7R34GhxN5lvWew+SElMC//zhItOkiY8kVe6TNCKopaBjqd4CzHBYgSm5GvmU7XIfI8AEYLFOBZhFRUUEkQDCm9kZmJJUQ4AZdVZ91P3wUpJaHdIr/5TSe0h9h3f/8fzNWxdyAGhv0HjPAFNybSMPRoC5Y53UYtL4k+3j7jJNmvhIdCIPQSnNNeYXssIowGyPLuQALNapAHP79u0BLgMAAszrGZgEmFFnzaf+eyzDKb34N+lHf/M+x6zgbJUcnIEJoJMGHOXarm043cc3LpUmnx345/d2/uWAowL/3OEkNVOKjfcMewkwQ05Ko0nYl5oVPs30TAPMGtf7C3tM8OsBANHEB0C0iEswX31GE5/o0toibV/t38esrZBe+ntodYE1a+Ajhc8PTgCCKylVKh7mOb5zvSu0CLQtX3mOJaZIhf0D/9zhxGaTMky2kRNghpyUZpMAMxw6kB9iFmDKCM7rAQB40ekzMAEg7KVmeTY2qTloTS2wxu7NUrPJ1uru2r5a+vAZadpl/n9sX5htH5cIMAF4N2i85y94DKfr2I2BAVwJWV8j7d/pOV4yipVeZjLzpPLd7mOVZdbUAnOtLUpuqvUcD5fzLyVXEx8zdVXej2UCgAAjwAQQPdKyPN/0swIzumz92nx8xjVSfnHnHuOjl6TNX5qP9xniCgGs5jXA5AxMAF4MGi+9N9dz/O2HpbeDXo3Un+3jpswa+VSXu3YYxPCjXUioOiC7DM/xsF+BKRr5ALAU3+UARA+z3xhzBmZ02bbScyw2XjpqmhQX37nHyO0jPfgzqcpkxcvL97rOw8zK616d3eVtlSlnYALwJqfQFbAcLLW6Epf+o62uIDRlmTTyMZxS1YHwWuEXwWwVe80v5ERAgEkjHwAW4gxMANEj1aTJSmOd5DDpfIrI09Qg7droOV40tPPhpSQlp0kX3iTZTX4H2FgrPX+31OLwvU5/aH9UwiFsIQfgjc0WGivIJSmnl2urNDx5+3fhHMyQYTvoJcBkBSYAdAsBJoDoYRZgSmwjjxY710rOFs/xklFdf6xeA6XTrjK/VrpFeufRrj+mP3EGJgBfDD/W6gpchh1jdQWhiwAz9FV4WcVMgAkA3cIWcgDRI81bgFlh/ZZfBN5Wk+3jklTi4zbFo0+Tdq6TVn/keW3ZO1LREGnUVN8e+3CVZdLGpZK3FR1myr4xH+cMTAAd6TPE9dq29B3rasjvK006y7rnD3UEmCHPdAVmSmZ4/RIxMcXVRMvZ6j5OgAnAQgSYAKKHt66JnIMZHcwCzKRUqWdf3x7PZpNm/ljau006YBIYvnG/1LNEyuvj2+M7mqWPX5I+ftnVnMEfOAMTwJGc8SNp9Imu17b24UWgZeZL/UZIcQnBfd5wkpLhOru5pd3xNwSYIcP0DMxwO5/UZnN1Im//HpkAE4CFCDABRA+2kEev2kpp33bP8b4jXSsMfJWQJF34C+nhX0iORvdrjibp+bukq//S9VUXW76W3nrQ/800wmn1BwDr9B7kuiH02GyuVZjtf3FGgBkaWlvN/1uE0/bxQ1IyCDABhBTOwAQQPbwFmDUHg1sHgm/bKvNxX86/bC+vj2slppkDu6U3/iMZRuceq7ZSeukf0hO/8394GRvv2hIGAAhvZtvICTBDQ1WZbGYrl8OpA/khZudg0oUcgIUIMAFEj6RU887RrMCMfNv8fP5le6OOd50bZ2b1x9LStzv+fKdTWvae9O/rpFWL/VNTe0MmSrFxgXlsAEDwmAWY1QelFkfwa4G7SOhAfohZgMkKTAAWIsAEED3sdinV5M0YZ2BGNsNwbcluLyPXv2dSnXqVVDjA/No7j0nfbDS/tm+H9NivpTfvlxrr/FfPIYmprvPsZv3E/48NAAg+00Y+hlR1IOiloB1vuyciJcBsrCMoB2AZzsAEEF1Ss6TqcvexGgLMiFaxV6oq8xwvGeU6S8xfYuOkC26SHvyZ1Fjrfs3ZIr3wV+lHf3Udii9JzY3SouelJa8fuVFG4QDXNvWCEv/VCwAITx11Ig/HrcqRxGuAGWZNfCTzAFOS6muk9Ozg1gIAYgUmgGiTZnIOJlvII5tZ93HJf9vHD5eVJ517g/m1qjLp5Xtd28U3LpPuu1765JWOw8uEZGnG1dIP7yS8BAC4ZHUQYMJaZgFmcnp4nkF96Beu7bGNHIBFWIEJILqYNfKpq3SFSvYA/07HMFxhaWcbuhwuJlZK8fJGEh3zFmD2GxmY5xs0XjruPOmjlzyvbf7StULTrCN6e8Mnu7als8oBAHC4jlZgwlpmAWY4bh+XvK/AJMAEYBECTADRxSzAdLZKDTXe36j5w8rF0jv/7V73xpxCaeZPpL7D/VdXpHM6zRv45PeVUjMD97wnXCLt2iBtX+157UjhZWa+dMY10sCxASkNABDmktOluATJ0eQ+ToBpLWerVLHPczxct/V73UJOgAnAGmwhBxBdvIVWgTwH88Bu6ZV7uxdeSlL5HunpP0oV/IDSaXu3SQ21nuOBWn15SEyMdP7PzANzb+wx0pTzpJ/cS3gJAPDOZjNfhUmAaa2qcqm1xXM8bFdgsoUcQGghwAQQXczOwJQCew7mykWS4fTPYzU3Sh885Z/HigZmqy+lwJx/2V5qpnTBzyVbJ77VFg2V/u/v0vTLpPiEgJcGAAhzBJihJ5I6kEtSSqb5OAEmAIsQYAKILt5WxNUeDNxz7ljr38dbtVgq3erfx4xUZudf2mOk4mHBef7iYdL073u/npQqzbpWuuKPUl5RcGoCAIQ/swCz5qDU4gh+LXCJtAAzPlGKifMcr+vmjiIA8BEBJoDo4i3ADNQW8haHtHuT/x93/uP+f8xI42g2D497D5ISkoJXx7FnSUMneY6PPkG67t/S2OmBbyAFAIgs3hr5VJUFtw58x2uA2TO4dfiLzWZ+DiYrMAFYhCY+AKKLtzMw9+8MzPOVbpFamj3HBx8tFXViFaBhSIue8zyof+vX0pYVUv8x/qgyMn2zwfzfPhjbxw9ns7nOw/zoRWnTl1JGD+no06V+I4JbBwAgcngLMCv2u5r+IfjMAsykNNdui3CVkiFVH3AfI8AEYBECTADRJTZOyu0jle1yH9++xhUW2mz+fb4d68zHj5nV+QCruUFa/ILn+PzHpX6jWL3njdn2cUkqGRXcOiRXU58TLnLdAADoLm8BJudgWufgXs+xcO1AfohZIx+6kAOwCD/1Aog+xcM9x2rKzd94dtdOkwDTHiv1Gtj5xzj2bCnZ5A3k3m2u8zBhzizAjE/s2r89AAChKIsAM6Q4nebvI8P1/MtD2EIOIIQQYAKIPn29rHzcvtq/z+N0mgeYhSVd6zSdmCxNvdD82gdPu856hLuGOmnPZs/x4uFSDJsPAABhLinN9Uu59ggwrVFdLrWaNFCKxACzudHzaCMACAICTADRx1sH6h1r/Ps8ZbukxlrP8c6cfdneuFOkLJND4KvKpKVvd/3xIt321ZLh9BwP9vmXAAAEgs1mvo2cANMakdbA55BkkwBTohM5AEsQYAKIPmlZUo9enuPbV7vOwfQXs9WXklQ0tOuPFRsnTbvU/NpHL7pWHOI720Lo/EsAAAKBADN0eA0wI3AFpsQ2cgCWIMAEEJ3MtpFXl0sV+/z3HF4DzCG+Pd6wY6XCAZ7jDbXSxy/59piRauvXnmMpmVJeUdBLAQAgIMwCzNoKtvdagQATAAKOABNAdDJr5CP59xzMHWs9x3L7mDfk6Qy7XTp5tvm1z9+Sqg749riRprpcOrDbc7xklP+7zAMAYBVvnch5PxB8Jg18jMQUKTnNgmL8yKwLuUQncgCWIMAEEJ36egsw/XQOZuV+qdrkBwhfto8frt8IaeA4z/GWZmnhM9177Ehh1n1cYvs4ACCyeAsw2UYefOV7PIaMcF99KbECE0BIIcAEEJ3SsqWcQs/xHWv8cw6mt+3j3hoIdcX070syWUm4YqG0b0f3Hz/ceQsw+xFgAgAiCAFmaHA6zY8gMmu+GG68NvEhwAQQfASYAKKX2TmYVWX+eeNvtn1c8q0DeXv5xdKYE00uGNL7T3T/8cOZYZiff5ldIGXmBr8eAAACxVuA6c/zvHFkNQddO2HaMSIhwIxPkOISPcfpQg7AAgSYAKKXWYAp+WcbudkKzIxc/4VoJ14ixcZ7jm9aLm3z4zme4absG1cDg/ZKRge/FgAAAikpVYpP8hyvLAt+LdHMSwMfIzsCAkzJfBs5KzABWIAAE0D0ClQjn/pqqWyX53h3z788XEYPaeIZ5tfm/88/2+DD0TbOvwQARAmbTcrK9xxnC3lwee1AHsEBZj0rMAEEHwEmgOiVnu3aWtxedwPMnevNx/0ZYErSlPNcqy/a27NZWvOpf58rXJhtH5dN6jcy6KUAABBwZjs7CDCDy6QDuRQhW8gl807krMAEYAECTADRzawbeVWZVNGNN/87vZx/6Y8GPodLSpGOO9/82oInpBaHf58v1LW2mm+fL+xvHvQCABDuzM7BrKuUmpuCXkrUMlmB2RwTLyWlWVBMAHjbQh6tu30AWIYAE0B083YO5o5urMI0O/8yMVXq0dv3x/RmwgzX2ZrtVeyTlr/n/+cLZXs2Sc0NnuNsHwcARCpvjXyqWIUZNOWeAWZdQppri38kMAswW5ql5sbg1wIgqhFgAohuXs/B9LGRT3OTtGeL53jRUMkegJfc2DjppO+ZX1v0vNRY7//nDFVbV5mP08AHABCpvAWYbCMPDsMwXYFZF2+y7TpcJXv5WthGDiDICDABRLeMHpLZGUW+Bpi7N0rOVs/xYj+ff3m4kcdLPft5jtdXS5++GrjnDTVm51/GxEl9Bge/FgAAgiHTpImPRCfyYKmpcK1GbKcuMYICTLMVmBIBJoCgI8AEALNt5JX7fHvzb7Z9XPJ/A5/D2e3SyZebX1vyulR9MHDPHSqaG6VdGzzHi4ZKcQnBrwcAgGBgBaa1Du4xHa6Lj5DzLyXvASadyAEEGQEmAJg18pGkHT6swtxh0sAnNl4q6N/1x+qK/mPMt0o7mqQPnw3sc4eCneskZ4vnOOdfAgAiWVKKlJDsOV6xL/i1RCMvHchrE1iBCQD+Fmt1AQBgOa/nYK6WRp/Q+cdpbZW+MVkF2HuQ66zKQDv5cunBn3mOf7VAmjRTyu0T+BqsYrZ9XOL8SwCAh8rGRn1VWqptlZVydtBJubW1VavKy7V3xQrFxMQEscKuOSchTTlN7mde79+zXa9/+aVFFUWP8Wu/0hiT8Xn1LVoX4vPmSOw2mxJjY5XZXKcZJtcrDuxVU22tkmJjlRgbq/iYGNkipXERgJBEgAkAmblSVr7naoWunoO5b7t5R8ZAbh8/XEGJ6zzMVYvdxw2n9OivpaTU4NSRkCwNGCNNPFNKzQzOc25d6TmWmCIVmJwNCgCIGgcbGvRlaamW79mjL/fu1fI9e7SloqJrD7JrV2CK85MeRoPObj9YuV9Xv/GGBdVElxeMbR4BZpXsumv3PmnPPCtK8rt4w6kmk/HHPl2sny35rnGlTVJibKwSYmNlJ8j0maO5WXEbTBZEAN96/OyzdcagQVaXYQkCTACQXKsw2weYFXulqgOuRj+dYbZ9XJKKhnWvtq446VJp7adSa7vt1A01rluwlG6Rlr0nnXGNNHyyFMg3snVV0t5tnuP9Rkr28F35AADomgP19W1h5fLSUn357SrLSLdNnmc956lFyUar6m18HwykASbR3mYlBPZ9T5A12+yqNOzKlNNtPE/u7zUNSQ0tLWpoMTnSB13T0GB1BQhhza0mDWOjBAEmAEiuRj4rPvAc78o28p0mAabNHtwu2Fl50oQZruY9VmuokV78m7TmE+mMHwVuNea21ebjbB8HgIi25eBBvbRunT775hstLy3VzqroPJNvu+JNx4vl0DoRYAaMYWiAPDuQbzIJlMPdfsUps11Y2z7ABIBAI8AEAKnjRj6dCTANw7wDec++UkJSdyrruuPOk758X2p3HpZl1n3mCoJnXCONmOL/x/d6/iUNfAAg0pTX1+v5NWv0xMqVWvLNN1aXExK8BZj91KR1SgxyNdGjp1qU2m5VovTtCswIU6ZYDWoXYOYSYAIIMgJMAJCkzDzXrXK/+3hnz8Es32PejTGY28cPSU53bd1++Z7gP7c3DbXSS393rcY89Qf+fWyz8y/Te0jZBf59HgCAJRocDr25caOeXLVK8zZtUovTMzSKZt4CzL4mqwPhP2bbxyVps5f/HuFsv0lswApMAMFGgAkAh5htIz9YKlWXS+k5HX+u2epLSSq2IMCUpFFTpeQMae0nUk0XmxV0R0OteSf2Q9Z/rtjtq9U7f6xr1Wp3HdwrVe7zHC8ZFVHnTwFAtHEahhbv2KEnV67UC2vXqrrJPCzqiji7XSPy8jSuoEBjCwp0VEGB0hO8r5ZzOBz66KOPdNxxxykuLq7bzx8o9qZ66ZEbPcZ/P2a4rp18fvALihIZ6z6RPtjsMf6bWedr/KbSkJ83R9LqdKqxpUWNLS3q9/Gz0qbP3K73tLXqzpNOUkNra9v9GhwONUbx+Xzd5XQ6tfubb9Srd2/Z7Xary0GIKsrIsLoEyxBgAsAhxcO9nIO5Rhp1fMef67WBT5A6kJsZMMZ1CybDkNZ8Ks17SKqvNr2LrbFO43Z8JOcLDdKsH0tp2b4/3zaT1ZcS518CQJhavX+/nly5Uk+tWqVvqs2/j3RGfEyMRh4WVo4rLNTIvDwlxHb+xx+Hw6HtiYkalpsb+kFUYqrUWOs21KO5Rj1ycy0qKAqsrDUdLu43VH12VYTHvOmszb2lTe5DMYZTvxw/VkpKsaamCORwODRv3jzNmDEjcuYO4EcEmABwSN8R5uPbVx85wDRbgZldELjGNaHKZpNGTHadKTrvIWntEq93tW9aJt13vXT6D10rRn1ZMWm2fVxydSAHEFQtTqc+++Ybrdi7V4Y/Vlj7SWtrq9aUlWnb0qWKiaGhSaiqamrSi2vX6ut9JqvqjyDWbtdRPXu6hZUj8vIUH03/vTPzpL3tArWK/eb3hX+Ul3qOxSdKKRG4Osrb11RXSYAJIGjCPsA8ePCg/t//+3964403ZLfbdd555+nee+9Vamqq18854YQTtGjRIrexH/3oR3rggQcCXS6AUJaVJ2XkSlVl7uM7jnAOZs1BqWKv57hV28dDQWqmdOEvXGdevuV9NaYa66RX7nXd78wfS+ldWI3pdJoHmHlFUlqWT2UD6DrDMPTWpk36+XvvaUN5udXleLd7t9UVwM+O6d1bl40cqQuHD1duSpSHKJm50t6t7mPtz/WGfx00ee+XXRCZR9h4DTCrpB69glsLgKgV9gHmpZdeqtLSUs2fP18Oh0NXXnmlrrnmGj399NMdft7VV1+t22+/ve3j5OTkQJcKIBz0HS59/aH7WPkeV0jpbavzDi/nX1rRwCfUDJ/sWtk672FXSOnNxmXSf66XppwnpaR37rHrqqSGGs9xto8DQbNq3z7Nee89vb9165HvDPhB/6wsfX/UKF06apQGZHfjCJJIk5nnOdZQIzU1SAlJwa8n0hmG65z09iK1gaC3ANPbL6gBIADCOsBct26d3nnnHS1dulTjx4+XJP3rX//SjBkz9Ne//lWFhYVePzc5OVk9e/YMVqkAwkXfEZ4BpuQ6B3Pkceaf462Bj5XnX4aSlAzpgp+7wsy3HjTv1i65VmO+/3j3n69kVPcfA0CH9tXW6rcLF+qRr76SM4S2iyMy5SQl6eIRI3TZqFGa2KuXbJG4wq27svLNxyv3S/nFwa0lGtRVSc0NnuPRFmB6e08HAAEQ1gHmkiVLlJmZ2RZeStL06dNlt9v1+eef65xzzvH6uU899ZSefPJJ9ezZUzNnztStt97a4SrMpqYmNR3W/bD620PFHQ6HHA6HH74aaxyqPZy/BlgjYudOr8EyOzK7detKOYccY/opsTvWqP2PUkZKplrScqRI+/fpjoHjpWsGyfbOfxW7zvvZmN1h2GPUUjiIf/cIFLGvOWGmsaVF//ziC9316aeqaW62uhxEsMTYWM0cOFDfGzFCp5SUKO7b8yxbWlqCVkM4ve7YUrNNf7BrKS+Vke19UQd8Y9u/y/zfOyM3rOZNp8Unm78/rj4oZyR9nRaLyLmDoAj3udPZusM6wNy7d6/y8ty3S8TGxio7O1t795qcSfKt733veyouLlZhYaFWrlypX/7yl9qwYYNefvllr59zxx136Pe//73H+HvvvRcR28/nz59vdQkIUxE3dwxDJ8elKNlR5zZcv/YLfaDeHnePbW3WjH3bPcb3xGVq2dtvB6rK8JY4WAX9EjVq1xIltjT69aEPJuXo4wUL/fqYCC0R95oTJgzD0CeVlXq8tFT7CS4RIDZJI1JTdUJWliZlZio5JkbatEnzN2064ucGUji87qQ1VOgkk/G1n36obZu63hgJHetTvlljTcaXbNyhg3tc723CYd50ls1wapbJ+I51q7SqJvx/Fg41kTR3EFzhOnfq6+s7db+QDDBvvvlm3XXXXR3eZ906L1s2O+Gaa65p+/vIkSNVUFCgadOmacuWLerfv7/p59xyyy2aM2dO28fV1dXq06ePTjnlFKWnd/K8thDkcDg0f/58nXzyyYqLM/u9GmAukudOTMtWafVHbmNpTdWacfyxHl3FbVtWyGbSR6bnhBM04+gZAawyfB2aOyPOvEjOhU/Kvvpjvz125vFnacbYk/32eAgdkfyaE+qW7tmjn7//vpZ8880R75ufkqLfHX+8TuzbN/CFdVJLS4s+/vhjTZkyRbGxIfnWF9/KT0lRSny81WW0CavXnaZ6af1rHsPDe+Vq6HTej/ib/cNnpZ2e48ecfrYcianhM2+6wNjwimztzh7vm5ulPjOYX/4SVq85CCnhPncO7XA+kpB8F/ezn/1MV1xxRYf3KSkpUc+ePbV/v3t3vZaWFh08eLBL51tOnDhRkrR582avAWZCQoISEhI8xuPi4sJygrQXKV8Hgi8i507JKI8AU5Lidm+QRkxxH9y9wfQhYvqNVEyk/bv4WVx6tuzn/8z1b7rgKalsVzcezSaNna6Y8acoJiYkv7XBTyLyNSdE7aqq0i0LFuipVauOeN+EmBjNmTRJt0yZojST90tWcjgc2pyQoEG5ucwd+CQsXnfiMqSkVKmh1m04pvoA70cCwazDe1yi4rJypW+POQiLedMVKRkezRPt9dWyR9LXGCIibu4gaMJ17nS25pD8KS83N1e5ublHvN+kSZNUWVmp5cuXa9y4cZKkDz74QE6nsy2U7IwVK1ZIkgoKIvTQZQBd03e4+fj2NZ4BplkH8vgkDszviiETXbeqA65GPr7I6CElpvi3LiAKVTY2antlpV5et05//fRTNXTivMGLhg/XndOnq29mZuALBOBdZr5HgGkatKH7TDuQ95QiucFUSrp0oN0YXcgBBFFIBpidNXToUJ122mm6+uqr9cADD8jhcOi6667TxRdf3NaBfPfu3Zo2bZoef/xxTZgwQVu2bNHTTz+tGTNmKCcnRytXrtRPf/pTHX/88Ro1is61ACRl9ZTScqSacvfx7avdP25xSLtNzuXqM0SyxwSuvkiV0cN1AxAwtc3N2lZRoe2VldpWWen25/bKSlU2dv5c2om9eukfp56qSX36BLBiAJ2WmSeVbnEfI8D0P8PwEmBG+GIYs07kdCEHEERhHWBKrm7i1113naZNmya73a7zzjtP//znP9uuOxwObdiwoe1Q0Pj4eL3//vu65557VFdXpz59+ui8887Tb37zG6u+BAChxmZzrcJctdh9/MA3Um3ld+dg7tkstZp0TCseGugKAYSZqsZGPbt6tbZUVAT1eVucTu2qrnYFlRUVKm9o6PZj9klP153Tp+viESNkj+TVRkC4yczzHGuolRrrpUQarfhNfbXrzNH2sjt/hFlYMgsw62skp1Oy24NfD4CoE/YBZnZ2tp5++mmv1/v27SvDMNo+7tOnjxYtWhSM0gCEM7MAU5J2rJGGT/7272vNP7doWODqAhB2dlRW6qTHH9fWIIeX/pYSF6ebp0zRnEmTlByG5ysBEc8swJRcqzB79g1qKRHNbPWlJOUUBreOYEs2CTANpyskTwnfprYAwkfYB5gAEBB9R5iPb1/9XYC50+T8S3us1GtA4OoCEFYqGxs14+mnwzq8tEm6cswY/fGkk1SQlmZ1OQC8yfTSQ4AA078O7jUfj8YVmJJrGzkBJoAgIMAEADPZBVJatlRz0H18+xrXn06ntGu95+f1GiDFhVYHXgDWaGpp0TnPPae1ZWVWl+Kzaf366S8nn6yjaHQIhL6sfPNxzsH0r/I95uMRfwaml5CyrkoSZyEDCDwCTAAwY7NJxcOl1R+5j5ftcp2DWVtp3jG7iPMvAUiGYegHr7+uD7dvt7qUI+qRnKy+mZnql5np9ueIvDz1yfCy4gZA6MnoYAUm/MdsBWZsvOsX35HM2wrMehr5AAgOAkwA8KbvCM8AU3Kdfemt6yIBJgBJty5cqKdWrbK6DElSZmKiRzjZNzNT/bKyVJyRobQEVo0DESEhSUpOdzWZORwBpn9560Ae6U3NOtpCDgBBQIAJAN70HW4+vmON5w8HkiQbASYAPbx8uf70kckvPyT1Tk/Xp1ddFdSzJGPpDgtEj8w8AsxAMgwvAWaEn38pmTfxkaQ6s/fEAOB/BJgA4E1OoZSaJdW2a76xbZXUWO95/7wiKSk1OLUBCElvb9qkH7/1lum19IQEzfve99iWDSBwMnOlPZvdxwgw/aehxvwIoUjvQC653uPa7K7O44djBSaAIOFX8gDgjc1mvgqzbJdUU+45zupLIKp9VVqqC154Qa2G4XEt1m7XyxdeqJH5XppsAIA/ZJq8xjTWSQ0moRu6Llo7kEuS3e46oqA9AkwAQUKACQAdKfayjdz0vsMCVweAkLazqkpnPP206hwO0+uPzJypaSUlQa4KQNTJzDMfZxWmf0RrB/JDzDqR08QHQJAQYAJAR/qO6Px9WYEJRKXKxkbNeOopldbWml7//QknaPaYMcEtCkB0IsAMLK8rMKMlwDQ5AoUVmACChAATADrSo5eUknnk+2XmSRk9Al4OgNDS3Nqqc597TmvKykyvXzlmjG49/vggVwUgahFgBpZZA5/YeCktO/i1WIEAE4CFCDABoCPezsFsj9WXQNQxDEM/eP11Ldy+3fT6ySUlevDMM2Wz2YJbGIDolZlrPk6A6R9mAWZWvut8yGhg1om8oVZqbQ1+LQCiTpS80gJAN3RmG3kR518C0ea3CxfqyZUrTa+Nys/XixdeqLiYmCBXBSCqxSear5IjwPQPswAzWraPS+ZzS5Lqq4NbB4CoRIAJAEfSmUY+xazABKLJI19+qT9+9JHptd7p6Zr3ve8pPSEhyFUBgMy3kRNgdl99jWu1YXs5BJhsIwcQDLFWFwAAIS+3t5Sc7v23y0lpUo/ewa0JgGXe3bxZ//fmm6bX0hMSNO9731OvdJNOrQAQDJl50u5N7mMV+8xXD6Lz9u80H4+qFZhevrexAhNAEBBgAsCRHDoHc+0S8+tFQ133ARDxVuzdq/NfeEGthuFxLdZu10sXXqiR+fkWVAYA3zJbgdncIP3zJ8GvJRpEVYDJCkwA1iHABIDO6DvCe4BZzPmXQCSqdzi0ct8+fVlaqi9LS7W8tFSr9+9Xi9Npev+HZ87U9JKSIFcJAO1460SOwCDAlOoqg1oGgOhEgAkAndFRIx86kANhr6apSSv27m0LKr8sLdW6AwfkNFlpaeZ3U6fqijFjAlskAHQGAWbwxMRJ6TlWVxE8Zl3IJamOLeQAAo8AEwA6I7eP+TmYcQlSASuuACscbGjQp7t2aUdlpToXM7qra27Wim9XWG4qL/fpMSTpijFj9NupU338bADws96Dpdh4qaXZ6koiX98Rkj2K+uImJkv2WMnZ4j7OFnIAQUCACQCdYbNJ/cdIqxa7j/cdIcXwUgoEw4HmZj27Zo2W7N6txTt2aE1ZmdUl6eSSEj105pmycQ4ugFCRlCKdPFt6+xHJ51/N4IiS06Vpl1pdRXDZbK5t5DXl7uMEmACCgJ+6AaCzTrhIWvfZdysaYmKlEy62tiYgQhmGoQ3l5fpoxw59tHOnPtqxQ9urqqS1a60urc3xxcV68cILFRcTY3UpAOBu4gyp/2hp5zpWYgZCSobUd6T3rtyRLCXdM8CkCzmAICDABIDOyimUrvu3tPxdyemUxk53jQFoYxiGaYfuI3Eahlbt2+cKK78NLMvq6wNQoe8GZGdrbEGBxvbsqWP79NHkoiLZWXkJIFT16OW6Af5k1siHFZgAgoAAEwC6IjNXmnaZ1VUAIae0pkZz3ntPr65fr8aWliN/Qgiz22wa0qNHW1g5tqBAY3r2VEZiotWlAQBgrWSTVacEmACCgAATAAB0y2vr1+uHb7yhAyG2YrIzYu12Dc/NdYWVBQUaV1CgUfn5SomPt7o0AABCj9kKzKZ6qcUhxcYFvx4AUYMAEwAA+KSuuVk/ffddPfzll0F/7l5paTquuFjHFRXp2D59lJ6Q0OXHsNts6pmaqsRY3g4BANApZgGm5FqFmdEjuLUAiCq8YwcAAF22dPduXfryy9p08GBQnm9QdraKJV08ebJO7NdPfTMz6fwNAECwEWACsAgBJgAA6LRWp1N3fvyxfrdokVqczoA8h91m05iePXVcUZGOKyrSlKIiZSckaN68eZoxcqTi4tiiBgCAJbwFmHQiBxBgBJgAAKBTtlVU6PuvvKJPdu3q8H4zBg7U8NzcLj9+Wny8JvTqpUkmW8IdDkeXHw8AAPhZRyswASCACDABAECHDMPQkytX6tp581TT3Oz1fqnx8frX6adr9ujRbO8GACASmXUhlwgwAQQcASYAAPCqoqFBP37rLT23Zk2H9zumd289ec456p+dHaTKAABA0LECE4BFCDABAICphdu26fJXX9U31d7PtYqx2XTr8cfr18cfr1i7PYjVAQCAoItPlGLjpZZ2OzIIMAEEGAEmAABw09zaqls/+EB/+fRTGR3cryQrS0+de66O6d07aLUBAAAL2WyuVZhVZe7jBJgAAowAEwAAtNlWUaFzn39eK/bu7fB+V44Zo3tPO01p7ZrtAACACGcWYNKFHECAEWACAABJ0oH6ep34v/9pR5X3VRTZSUl66Mwzdd6wYUGsDAAAhAyzczBZgQkgwAgwAQCAWpxOXfTiix2Gl9NLSjT3rLPUK91LB1IAABD5zDqRE2ACCDACTAAAoF/Mn68Ptm0zvRYfE6M7p03TDcccI7vNFuTKAABASDFbgelokpobXU1+ACAACDABAIhyT65cqX989pnpteG5uXr6vPM0Kj8/yFUBAICQZBZgSlJdNQEmgIAhwAQAIIp9WVqqq994w/Ran/R0fTB7tvJSUoJcFQAACFkpXo6Sqa+SsvKCWwuAqGG3ugAAAGCNA/X1Oue559TY0uJxLSEmRi9fdBHhJQAAcOd1BSbnYAIIHAJMAACi0KGmPTu9NO15aOZMjS8sDHJVAAAg5KVkmo8TYAIIIAJMAACiUEdNe66fMEGXjx4d5IoAAEBYMOtCLrnOwASAACHABAAgynTUtGdqcbH+esopQa4IAACEDW9nYLICE0AAEWACABBFjtS05/kLLlBcTEyQqwIAAGEjLsG82zgBJoAAIsAEACBKlNXVeW3akxgbq1do2gMAADrDrJFPPQEmgMCJtboAhJbGlhY1mfxgC0+xdrviY2IUa7fLZrNZXQ4AdOiITXvOPFPjaNoDAAA6IyVDqtjnPsYKTAABRIAJN39avFh//Ogjq8sIO/ExMYr7NtA8dIs77O+HX4+xB3fhs01Sany80hMSunRLiIkhmAUiyE3vvaeF27ebXrth4kR9n6Y9AACgs5JNVmASYAIIIAJMwA+aW1vV3NqqOofD6lL8JtZuV9wRwtbW1lbFrF4dpIoQLmw2m1Li4pQaH296S4mN1b49e/TVxx8rIzHxu/H4eMX6GPBnJCRoSI8eyklO9vNXExme+Ppr3fP556bXTujbV385+eQgVwQAAMKa2RbyumrJMCQWQQAIAAJMAKZanE61OJ2duCNHDsBTvcOhsvr6Du/zyv79fn/e3ORkDc3N1dAePVy3b//eOz09alcUf1laqmvefNP0WlFGhp4//3ya9gAAgK4xCzBbHVJTg5TIL5QB+B8BJgAgYpTV16tsxw4t3rHDbTw1Pl5Dvg01hx0KOHNzVZKV5fOqz3DQmaY9uTTtAQAAXZWSbj5eV0WACSAgCDABABGvtrlZy/bs0bI9e6wuRUmxscpMTDziLSMhwe3jpLg4dWUNqSFp9quvem3a8/DMmRpbUOCXrwkAAEQZsxWYkqsTeQ7vLwD4HwEm3JzUr19Er0byF0OuLdbNra1yfHv+ZXNrq5qdTvePW1vl+PZ+h25Owwhqra1Op2qam1Xd1KTqpqagPz8Adw0tLWqorVVpba1lNdw4caIuGzXKsucHAABhzluASSMfAAFCgAk3J/brpxP79bO6DASIYRiqdzjcAs2Obh2FnU6nUzt37lRRUZHshN44TKvTqTqHQ7XNzaa3mubmzp2vioA4sW9f/eWUU6wuAwAAhDOzLuQSASaAgCHABKKIzWZTyrfdnnumpnbrsRwOh+bNm6cZp5+uuLg4P1WIaOBwOPTam2/quGnT1GQYbuGmLyuEnYahHZWVWnfggOtWVqYdXrZNR7uijAw9d/75rLQHAADd43UFZnVw6wAQNQgwAQBBF2e3KzspKWDhd11zszaUl2tdWZnWHTigtd/+ufngwahd/UnTHgAA4DfJaebjrMAEECAEmACAiJMSH6+xBQUeTWocra3afPCg1h04oPUHDuhAfX1Q6zK+XXFa2dSkysZGj1ugwtWk2Fg9de65NO0BAAD+ERsnJaZIjXXu4wSYAAKEABMAEDXiYmI0NDdXQ3NzrS7Fw6EzaqtMws2KhgY1t7b69LgZiYk6qV8/9c3M9G/BAAAguqVkeAaY9WwhBxAYBJgAAISAw8+oLUzzsi0LAAAgVKRkSOV73MdYgQkgQDjFHwAAAAAAdE1yuucYASaAACHABAAAAAAAXWPWiby+WorShokAAosAEwAAAAAAdI1ZgOls9TwXEwD8gAATAAAAAAB0jVmAKbGNHEBAEGACAAAAAICu8RZg0okcQAAQYAIAAAAAgK4xa+IjsQITQEAQYAIAAAAAgK5hCzmAICLABAAAAAAAXUOACSCICDABAAAAAEDXJKdJsnmOE2ACCAACTAAAAAAA0DX2mG9DzHYIMAEEAAEmAAAAAADoOrNt5HQhBxAABJgAAAAAAKDrzDqRswITQACEfYD5pz/9Sccee6ySk5OVmZnZqc8xDEO//e1vVVBQoKSkJE2fPl2bNm0KbKEAAAAAAEQSsxWYBJgAAiDsA8zm5mZdcMEF+vGPf9zpz7n77rv1z3/+Uw888IA+//xzpaSk6NRTT1VjY2MAKwUAAAAAIIKYbiGvkZytwa8FQESLtbqA7vr9738vSZo7d26n7m8Yhu655x795je/0VlnnSVJevzxx5Wfn69XX31VF198caBKBQAAAAAgcpgFmDJcIWZqZrCrARDBwj7A7Kpt27Zp7969mj59ettYRkaGJk6cqCVLlngNMJuamtTU1NT2cXW162Bih8Mhh8MR2KID6FDt4fw1wBrMHfiKuQNfMG/gK+YOfMXcgS+ibd7YE1MVYzLuqD4oJaQEvZ5wFm1zB/4T7nOns3VHXYC5d+9eSVJ+fr7beH5+fts1M3fccUfbas/Dvffee0pOTvZvkRaYP3++1SUgTDF34CvmDnzBvIGvmDvwFXMHvoiWeVNQuV0TTMa/+OA9HUgrCHo9kSBa5g78L1znTn19fafuF5IB5s0336y77rqrw/usW7dOQ4YMCVJF0i233KI5c+a0fVxdXa0+ffrolFNOUXq6See1MOFwODR//nydfPLJiouLs7ochBHmDnzF3IEvmDfwFXMHvmLuwBfRNm9sO9dK2z70GJ84cqiMYccGv6AwFm1zB/4T7nPn0A7nIwnJAPNnP/uZrrjiig7vU1JS4tNj9+zZU5K0b98+FRR89xuhffv2acyYMV4/LyEhQQkJCR7jcXFxYTlB2ouUrwPBx9yBr5g78AXzBr5i7sBXzB34ImrmTXqO6XBsY50UDV9/AETN3IHfhevc6WzNIRlg5ubmKjc3NyCP3a9fP/Xs2VMLFixoCyyrq6v1+eefd6mTOQAAAAAAUc20iY+kusqglgEg8tmtLqC7du7cqRUrVmjnzp1qbW3VihUrtGLFCtXW1rbdZ8iQIXrllVckSTabTTfeeKP++Mc/6vXXX9eqVat0+eWXq7CwUGeffbZFXwUAAAAAAGEmMUWymcQKdVXBrwVARAvJFZhd8dvf/lb/+9//2j4+6qijJEkLFy7UCSecIEnasGGDqqq+ewH9xS9+obq6Ol1zzTWqrKzUlClT9M477ygxMTGotQMAAAAAELbsdtcqzNoK9/H6zp1pBwCdFfYB5ty5czV37twO72MYhtvHNptNt99+u26//fYAVgYAAAAAQIQzCzBZgQnAz8J+CzkAAAAAALBIcrrnGAEmAD8jwAQAAAAAAL4xa+RTxxZyAP4V9lvIAQAAAACARVJMVmA21rrOwbQTOXRai0Oxrc1SU73UGmd1NQhVcfFSTHT+fxWdXzUAAAAAAOg+sxWYknT37ODWEebiJJ0hSSuftrgShLSLfikNPcbqKizBFnIAAAAAAOAbbwEmAPgRASYAAAAAAPANASaAICDABAAAAAAAvuk1ULIRLQAILF5lAAAAAACAb9KypclnW10FgAhHEx8AAAAAAOC76d+XBo6Tdq6TWhxWVxOWWp2t2rRpkwYOHKgYe4zV5SBU9ehldQWWIcAEAAAAAADdUzzMdYNPnA6HNtTOU//jZygmLs7qcoCQwxZyAAAAAAAAACGLABMAAAAAAABAyCLABAAAAAAAABCyCDABAAAAAAAAhCwCTAAAAAAAAAAhiwATAAAAAAAAQMgiwAQAAAAAAAAQsggwAQAAAAAAAIQsAkwAAAAAAAAAIYsAEwAAAAAAAEDIIsAEAAAAAAAAELIIMAEAAAAAAACELAJMAAAAAAAAACGLABMAAAAAAABAyCL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      "text/plain": [
       "<Figure size 1600x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_top5_centered_importance_v2(model_xgb, res_df, \"Age\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.41616586, 0.6294607, 0.6687761, 0.7147785, 1.5367968]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_top5_centered_importance_v2(model, data, col_name, absolute=False):\n",
    "    plot = plt.axes()\n",
    "    plot.figure.set_size_inches(16, 8)\n",
    "\n",
    "    if absolute:\n",
    "        title = \"Центрированный график изменения абсолютной важности переменных\"\n",
    "    else:\n",
    "        title = \"Центрированный график изменения важности переменных\"\n",
    "    plot.set_title(title, fontsize=18)\n",
    "\n",
    "    explainer = shap.TreeExplainer(model)\n",
    "\n",
    "    data_copy = data.copy()\n",
    "    if \"Survived\" in data.columns:\n",
    "        data_copy = data_copy.drop(\"Survived\", axis=1)\n",
    "\n",
    "    shap_values = explainer(data_copy).values\n",
    "\n",
    "    cols = []\n",
    "    indexes = []\n",
    "    mean_importance = list(np.mean(np.absolute(shap_values), axis=0))\n",
    "\n",
    "    max_list = sorted(mean_importance)[-5:]\n",
    "    for max_val in max_list:\n",
    "        indexes.append(mean_importance.index(max_val))\n",
    "        cols.append(data.columns[mean_importance.index(max_val)])\n",
    "\n",
    "    col_vals = list(sorted(set(data[col_name].values)))\n",
    "    res_vals = []\n",
    "\n",
    "    for val in col_vals:\n",
    "        new_col = len(data_copy[col_name]) * [val]\n",
    "        data_copy[col_name] = new_col\n",
    "        shap_values = explainer(data_copy).values\n",
    "        res_vals.append(shap_values)\n",
    "\n",
    "\n",
    "    res_vals = np.array(res_vals)\n",
    "\n",
    "    for i in range(0, len(indexes)):\n",
    "        res = []\n",
    "\n",
    "        for j in range(0, len(res_vals)):\n",
    "            val = res_vals[j, :, indexes[i]]\n",
    "            if absolute:\n",
    "                val = np.absolute(val)\n",
    "            res.append(val.mean())\n",
    "\n",
    "        plot.plot(col_vals, res, color=colors[i], linewidth=4, label=cols[i])\n",
    "\n",
    "    plot.grid()\n",
    "    plot.set_xlabel(col_name, fontsize=16)\n",
    "    plot.set_ylabel(\"Важность переменных\", fontsize=16)\n",
    "    plot.legend()\n",
    "\n",
    "    return plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_df_values_for_creation(data, delta=25):\n",
    "    result = {}\n",
    "    for key, data in data.items():\n",
    "        unique_data = set(data.values())\n",
    "        if key == \"Fare\":\n",
    "            result[key] = [10, 250, 500]\n",
    "        elif len(unique_data) <= 10:\n",
    "            result[key] = list(sorted(unique_data))\n",
    "        else:\n",
    "            result[key] = generate_values(min(unique_data), max(unique_data), delta)\n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_values(min_val: float, max_val: float, delta: float) -> list[float]:\n",
    "    values = []\n",
    "    current_val = min_val\n",
    "    while current_val <= max_val:\n",
    "        values.append(current_val)\n",
    "        current_val += delta\n",
    "    return values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Расчёт числа элементов декартового произведения\n",
    "def test(creation_data):\n",
    "    count = 1\n",
    "    for key, values in creation_data.items():\n",
    "        count *= len(values)\n",
    "        print(count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot = plt.axes()\n",
    "\n",
    "res_vals, col_vals = ice_plot_data_y(model, data, col_name)\n",
    "y_label = 'Вероятность удачного исхода'\n",
    "title = f'с-ICE график вероятности удачного исхода при изменении переменной {col_name}'\n",
    "\n",
    "df = pd.DataFrame(np.array(res_vals))\n",
    "df = df.T\n",
    "mean = df.mean()\n",
    "\n",
    "plot.figure.set_size_inches(16, 8)\n",
    "plot.set_title(title, fontsize=18)\n",
    "\n",
    "for i in df.index:\n",
    "    plot.plot(col_vals, df.loc[i], color='black', linewidth=0.1)\n",
    "\n",
    "plot.plot(col_vals, mean, color='lime', linewidth=6)\n",
    "\n",
    "plot.grid()\n",
    "plot.set_xlabel(col_name, fontsize=16)\n",
    "plot.set_ylabel(y_label, fontsize=16)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': 'с-ICE график вероятности удачного исхода при изменении переменной Age'}, xlabel='Age', ylabel='Вероятность удачного исхода'>"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_ice_plot(model_xgb, res_df, \"Age\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "def ice_plot_data_y(model, data, col_name):\n",
    "    data_copy = data.copy()\n",
    "\n",
    "    col_vals = list(sorted(set(data[col_name].values)))\n",
    "    res_vals = []\n",
    "\n",
    "    for val in col_vals:\n",
    "        new_col = len(data_copy[col_name]) * [val]\n",
    "        data_copy[col_name] = new_col\n",
    "        predict = model.predict_proba(data_copy)[:, 1]\n",
    "        res_vals.append(predict)\n",
    "\n",
    "    return res_vals, col_vals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_ice_plot(model, data, col_name, importance=False):\n",
    "    plot = plt.axes()\n",
    "\n",
    "    if importance:\n",
    "        explainer = shap.TreeExplainer(model)\n",
    "        res_vals, col_vals = ice_plot_data_importance(explainer, data, col_name)\n",
    "        y_label = f'Важность переменной {col_name}'\n",
    "        title = f'с-ICE график изменения важности переменной {col_name}'\n",
    "    else:\n",
    "        res_vals, col_vals = ice_plot_data_y(model, data, col_name)\n",
    "        y_label = 'Вероятность удачного исхода'\n",
    "        title = f'с-ICE график вероятности удачного исхода при изменении переменной {col_name}'\n",
    "\n",
    "    df = pd.DataFrame(np.array(res_vals))\n",
    "    df = df.T\n",
    "    mean = df.mean()\n",
    "\n",
    "    plot.figure.set_size_inches(16, 8)\n",
    "    plot.set_title(title, fontsize=18)\n",
    "\n",
    "    for i in df.index:\n",
    "        plot.plot(col_vals, df.loc[i], color='black', linewidth=0.1)\n",
    "\n",
    "    plot.plot(col_vals, mean, color='lime', linewidth=6)\n",
    "\n",
    "    plot.grid()\n",
    "    plot.set_xlabel(col_name, fontsize=16)\n",
    "    plot.set_ylabel(y_label, fontsize=16)\n",
    "\n",
    "    return plot"
   ]
  }
 ],
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